Quantifying sediment flux within rivers is a challenge for many disciplines due, mainly, to difficulties inherent to traditional sediment sampling methods. These methods are operationally complex, high cost, and high risk. Additionally, the resulting data provide a low spatial and temporal resolution estimate of the total sediment flux, which has impeded advances in the understanding of the hydro‐geomorphic characteristics of rivers. Acoustic technologies have been recognized as a leading tool for increasing the resolution of sediment data by relating their echo intensity level measurements to suspended sediment. Further effort is required to robustly test and develop these techniques across a wide range of conditions found in natural river systems. This article aims to evaluate the application of acoustic inversion techniques using commercially available, down‐looking acoustic Doppler current profilers (ADCPs) in quantifying suspended sediment in a large sand bed river with varying bi‐modal particle size distributions, wash load and suspended‐sand ratios, and water stages. To achieve this objective, suspended sediment was physically sampled along the Paraná River, Argentina, under various hydro‐sedimentological regimes. Two ADCPs emitting different sound frequencies were used to simultaneously profile echo intensity level within the water column. Using the sonar equation, calibrations were determined between suspended‐sand concentrations and acoustic backscatter to solve the inverse problem. The study also analyzed the roles played by each term of the sonar equation, such as ADCP frequency, power supply, instrument constants, and particle size distributions typically found in sand bed rivers, on sediment attenuation and backscatter. Calibrations were successfully developed between corrected backscatter and suspended‐sand concentrations for all sites and ADCP frequencies, resulting in mean suspended‐sand concentration estimates within about 40% of the mean sampled concentrations. Noise values, calculated using the sonar equation and sediment sample characteristics, were fairly constant across evaluations, suggesting that they could be applied to other sand bed rivers.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.4572","usgsCitation":"Szupiany, R.N., Lopez Weibel, C., Guerrero, M., Latosinski, F., Wood, M.S., Dominguez Ruben, L., and Oberg, K., 2019, Estimating sand concentrations using ADCP‐based acoustic inversion in a large fluvial system characterized by bi‐modal suspended‐sediment distributions: Earth Surface Processes and Landforms, v. 44, no. 6, p. 1295-1308, https://doi.org/10.1002/esp.4572.","productDescription":"14 p.","startPage":"1295","endPage":"1308","ipdsId":"IP-100807","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":467884,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/esp.4572","text":"External Repository"},{"id":361404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Argentina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -60.934295654296875,\n -32.11514862261243\n ],\n [\n -60.29296874999999,\n -32.11514862261243\n ],\n [\n -60.29296874999999,\n -31.421631960419596\n ],\n [\n -60.934295654296875,\n -31.421631960419596\n ],\n [\n -60.934295654296875,\n -32.11514862261243\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Szupiany, Ricardo N.","contributorId":189755,"corporation":false,"usgs":false,"family":"Szupiany","given":"Ricardo","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":757709,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lopez Weibel, Cecilia","contributorId":189756,"corporation":false,"usgs":false,"family":"Lopez Weibel","given":"Cecilia","email":"","affiliations":[],"preferred":false,"id":757710,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guerrero, Massimo","contributorId":213431,"corporation":false,"usgs":false,"family":"Guerrero","given":"Massimo","email":"","affiliations":[{"id":38756,"text":"University of Bologna, Italy","active":true,"usgs":false}],"preferred":false,"id":757711,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Latosinski, Francisco","contributorId":213432,"corporation":false,"usgs":false,"family":"Latosinski","given":"Francisco","email":"","affiliations":[{"id":38757,"text":"Universidad Nacional del Litoral, Argentina","active":true,"usgs":false}],"preferred":false,"id":757712,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wood, Molly S. 0000-0002-5184-8306 mswood@usgs.gov","orcid":"https://orcid.org/0000-0002-5184-8306","contributorId":788,"corporation":false,"usgs":true,"family":"Wood","given":"Molly","email":"mswood@usgs.gov","middleInitial":"S.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757707,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dominguez Ruben, Lucas","contributorId":213433,"corporation":false,"usgs":false,"family":"Dominguez Ruben","given":"Lucas","affiliations":[{"id":38757,"text":"Universidad Nacional del Litoral, Argentina","active":true,"usgs":false}],"preferred":false,"id":757713,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Oberg, Kevin 0000-0002-7024-3361 kaoberg@usgs.gov","orcid":"https://orcid.org/0000-0002-7024-3361","contributorId":175229,"corporation":false,"usgs":true,"family":"Oberg","given":"Kevin","email":"kaoberg@usgs.gov","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":757708,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202134,"text":"ofr20191011 - 2019 - Evaluation of Chinook salmon (Oncorhynchus tshawytscha) fry survival at Lookout Point Reservoir, western Oregon, 2017","interactions":[],"lastModifiedDate":"2019-02-21T16:47:19","indexId":"ofr20191011","displayToPublicDate":"2019-02-21T08:44:09","publicationYear":"2019","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":"2019-1011","displayTitle":"Evaluation of Chinook Salmon (Oncorhynchus tshawytscha) Fry Survival in Lookout Point Reservoir, Western Oregon, 2017","title":"Evaluation of Chinook salmon (Oncorhynchus tshawytscha) fry survival at Lookout Point Reservoir, western Oregon, 2017","docAbstract":"A field study was conducted to estimate survival of fry-sized juvenile Chinook salmon (Oncorhynchus tshawytscha) in Lookout Point Reservoir, western Oregon, during 2017. The field study consisted of releasing three groups of genetically marked fish in the reservoir and monthly fish sampling. Fish were released during April 18–19 (43,950 fish), May 30–June 2 (44,145 fish), and on June 28, 2017 (3,920 fish). Reservoir sampling began in May and occurred monthly through October, consisting of 5-day events where juvenile Chinook salmon were collected using various gear types (electrofishing, shoreline traps, gill nets). Data were analyzed using two models: (1) a staggered release-recovery model (SRRM), and (2) a parentage-based tagging (PBT) N-mixture model. The SRRM provided survival estimates from two periods: (1) mid-April to late May (SSRRM1), and (2) late May to late June (SSRRM2). Multiple estimates of survival were possible for each period using different combinations of recovery data from the three groups of fish that were released. Survival estimates for SSRRM1 ranged from 0.470 to 0.520. Estimates for SSRRM2 ranged from 0.968 to 0.969; cumulative survival from mid-April to late June (SSRRM2) was estimated at 0.870. We suspect that issues with the third release group led to biased survival results using the SRRM. The PBT N-mixture model provided survival estimates from six periods: (1) mid-April to mid-May (SNMIX1), (2) mid-May to mid-June (SNMIX2), (3) mid-June to mid-July (SNMIX3), (4) mid-July to mid-August (SNMIX4), (5) mid-August to mid-September (SNMIX5), and (6) mid-September to mid-October (SNMIX6). Survival estimates from the PBT N-mixture model were lowest for SNMIX1 (0.461) and increased monthly to a high of 0.970 for SNMIX6. Cumulative survival from mid-April to mid-July was 0.233 and overall survival from mid-April to mid-October was 0.188. This suggests that most mortality occurred early in the study when juvenile Chinook salmon were small. This could be because these fish were most vulnerable to predation in the reservoir at that time. We determined that mortality of juvenile Chinook salmon was high in the reservoir during this study and similar estimates of parr-to-smolt survival have been observed in other systems. Additional analyses are required, including results from the second year of study (2018), and potentially similar evaluations will need to be made at other locations to determine if reservoir mortality is a limiting survival factor for Chinook salmon in the Middle Fork Willamette River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191011","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and Oregon State University","usgsCitation":"Kock, T.J., Perry, R.W., Hansen, G.S., Haner, P.V., Pope, A.C., Plumb, J.M., Cogliati, K.M., and Hansen, A.C., 2019, Evaluation of Chinook salmon (Oncorhynchus tshawytscha) fry survival at Lookout Point Reservoir, western Oregon, 2017: U.S. Geological Survey Open-File Report 2019-1011, 42 p., https://doi.org/10.3133/ofr20191011.","productDescription":"vi, 42 p.","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-102234","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":361397,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1011/coverthb.jpg"},{"id":361398,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1011/ofr20191011.pdf","text":"Report","size":"12.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1011"}],"country":"United States","state":"Oregon","otherGeospatial":"Lookout Point Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.89718627929688,\n 43.91768033000405\n ],\n [\n -122.81410217285155,\n 43.98589821991874\n ],\n [\n -122.64244079589842,\n 43.929055415997134\n ],\n [\n -122.5140380859375,\n 43.82808744469062\n ],\n [\n -122.63626098632812,\n 43.77059798257491\n ],\n [\n -122.75711059570312,\n 43.854830911225235\n ],\n [\n -122.89718627929688,\n 43.91768033000405\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The Souris River Basin is a 24,000 square-mile basin in the Provinces of Saskatchewan and Manitoba in Canada, and the State of North Dakota in the United States. Above-average snowpack during the winter of 2010–11, along with record-setting rains in May and June of 2011, led to record flooding that caused extensive damage to Minot, North Dakota, and numerous smaller communities in Saskatchewan, Manitoba, and North Dakota. As a result, the International Souris River Board created the Souris River Flood Task Force to evaluate potential reservoir operation changes and flood control measures to manage future floods and droughts. Part of this evaluation involved identifying a need for a stochastic streamflow model to estimate the likelihood of future flooding or drought.
A stochastic natural (unregulated) streamflow simulation model described in a previous report was built upon in this report to include the effects of regulation of four reservoirs (Rafferty, Alameda, and Boundary Reservoirs and Lake Darling) and their operation guidelines. First, a regulated reservoir storage/streamflow routing model was developed and calibrated from when all four reservoirs were in operation until the end of the reconstructed natural streamflow dataset provided by the U.S. Army Corps of Engineers (1992–2011). The regulated reservoir storage/streamflow routing model then was combined with the stochastic natural (unregulated) streamflow model to provide a stochastic regulated streamflow simulation model for the Souris River Basin upstream from Minot, North Dakota.
The stochastic regulated streamflow simulation model was used to estimate regulated flood frequency curves, which are useful for feasibility and design of critical structures such as levees or bridges. Three potential future climatic conditions were considered in this analysis: condition A (wet equilibrium), representing wet (similar to 1970–2017) climatic conditions; condition B (transition), representing transition from wet to dry (similar to 1912–69) climatic conditions; and condition C (dry equilibrium), representing dry climatic conditions. Comparison of the estimated flood frequency curves for regulated flow among the three climatic conditions indicated large differences in flood magnitudes for the more extreme (1-percent or less) annual exceedance probabilities. The estimated 0.2-percent annual exceedance probability flood magnitude for the Souris River upstream from Minot, N. Dak., was 29,300 cubic feet per second for condition A (wet equilibrium), compared to 14,800 cubic feet per second for condition C (dry equilibrium). For comparison, the recorded peak streamflow for 2011 for the Souris River upstream from Minot, N. Dak., was 26,900 cubic feet per second. Although it is not possible to predict how long the current (1970–2017) wet climatic conditions may persist, flood risk for at least the next 25 years, or until about 2040, may be represented best by climatic condition A.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185155","collaboration":"Prepared in cooperation with the North Dakota State Water Commission","usgsCitation":"Kolars, K.A., Vecchia, A.V., and Galloway, J.M., 2019, Stochastic model for simulating Souris River Basin regulated streamflow upstream from Minot, North Dakota: U.S. Geological Survey Scientific Investigations Report 2018–5155, 24 p., https://doi.org/10.3133/sir20185155.","productDescription":"viii, 24 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-090130","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":361373,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5155/coverthb.jpg"},{"id":361374,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5155/sir20185155.pdf","text":"Report","size":"1.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5155"}],"country":"United States","state":"North Dakota","city":"Minot","otherGeospatial":"Souris River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.04052734375,\n 48.99824008113872\n ],\n [\n -104.74365234375,\n 49.42884000063522\n ],\n [\n -104.7930908203125,\n 50.004208515595614\n ],\n [\n -103.480224609375,\n 50.52041218671901\n ],\n [\n -102.0245361328125,\n 50.604159488561\n ],\n [\n -101.195068359375,\n 50.25071752130677\n ],\n [\n -100.65673828125,\n 49.745781306155735\n ],\n [\n -99.60891723632812,\n 49.648069803718805\n ],\n [\n -99.18594360351562,\n 49.577773933420914\n ],\n [\n -99.2340087890625,\n 49.39131220507362\n ],\n [\n -99.76547241210936,\n 49.413653634531116\n ],\n [\n -99.4482421875,\n 48.100094697973795\n ],\n [\n -101.502685546875,\n 47.99727386804474\n ],\n [\n -103.568115234375,\n 48.52388120259336\n ],\n [\n -104.04052734375,\n 48.99824008113872\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
The recovery of endangered Lost River suckers (Deltistes luxatus) in Upper Klamath Lake, south-central Oregon, has been impeded because juveniles are not recruiting into adult spawning populations. Adult sucker populations spawn each spring but mortality of age-0 suckers during their first summer is excessively high, and recruitment of juveniles into adult populations does not occur in most years. The last significant year class to join spawning aggregations was hatched in 1991. Capture rates for age-0 Lost River suckers decrease so substantially each summer that it is thought that mortality is nearly 100 percent within the first year of life each year. Causes of mortality are not understood but poor water quality, parasites, disease, predation, and non-native species are suspected to contribute to mortality. Upper Klamath Lake is hypereutrophic and summer water-quality conditions have large diurnal and seasonal fluctuations. Photosynthesis of Aphanizomenon flos-aquae, the most abundant cyanobacterium in Upper Klamath Lake, is responsible for large fluctuations in dissolved-oxygen (DO) concentrations and pH.
We introduced hatchery-raised, passive integrated transponder-tagged juvenile Lost River suckers into large mesocosms located at Fish Banks, Mid North, and Rattlesnake Point in Upper Klamath Lake, Oregon, to assess sucker mortality relative to water-quality conditions. We identified the date of death for each sucker by assessing movement patterns among vertically stratified antennas. We modeled daily mortality using known fate models relative to water-quality conditions measured by sondes. Histopathology was used to understand causes of eminent mortality for moribund suckers.
Fish mortality, growth, health, and movement patterns varied among locations, but it was unclear whether this variation was due to water-quality or other factors. Seasonal mortality was 58.8 percent at Fish Banks, 27.4 percent at Mid North, and 11.5 percent at Rattlesnake Point. Growth over the 109-day study period was lowest at Fish Banks (34.5 ±10.0 millimeters [mm] standard length (SL); 18.6 ±7.7 grams [g]), intermediate at Mid North (57.5 ±13.6 mm SL; 40.1 ±15.4 g), and greatest at Rattlesnake Point (78.4 ±13.0 mm SL; 72.5 ±18.7 g). Our ability to assess causes of juvenile sucker mortality in mesocosms using our modelling approach was limited by low daily mortality. Zero to 3 mortalities occurred per day, except on July 30 at Fish Banks when 7 mortalities occurred. Relative to any other measured and tested water-quality condition, mortality was more likely to occur on days with large fluctuations in oxygen percent saturation. When we assessed the fit of the most parsimonious model, performance was poor, which suggested that other factors were contributing to mortality. Our ability to assess the relationship between seasonal patterns in water quality and fish mortality were limited by the absence of substantial differences in water quality among sites, inconsistency in the depth at which measurements were collected, and no clear pattern in conditions leading up to and during mortality events. Except for DO at Rattlesnake Point and diel temperature variations at Fish Banks, seasonally summarized water-quality factors were similar among sites. The locations of water-quality monitors within the water column likely explain the differences in DO at Rattlesnake Point and temperature variation at Fish Banks. Furthermore, DO concentrations and other water-quality factors occurring during and prior to mortality events were inconsistent.
Microscopic assessments indicated severe gill hyperplasia, fusion of the secondary lamellae, and severe Ichthyobodo sp. infestations on the gills of most moribund suckers. Liver glycogen was usually depleted in suckers with severe Ichthyobodo sp. infestations. Ichthyobodo sp. infestations probably were the immediate cause of death and probably originated from the Klamath Tribes Fish Research Facility, although this parasite also is present in Upper Klamath Lake and severe water-quality conditions may have contributed to morbidity. As suckers in the mesocosms died, they were replaced with suckers from the Fish Research Facility that likely were heavily parasitized with Ichthyobodo sp. Therefore, it is possible that the gradient in mortality rate among sites was owing to site-varying differences in inadvertent increases in introduced parasite loads.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191006","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hereford, D.M., Conway, C.M., Burdick, S.M., Elliott, D.G., Perry, T.M., Dolan-Caret, A., and Harris, A.C., 2019, Assessing causes of mortality for endangered juvenile Lost River suckers (Deltistes luxatus) in mesocosms in Upper Klamath Lake, south-central Oregon, 2016: U.S. Geological Survey Open -File Report 2019-1006, 80 p., https://doi.org/10.3133/ofr20191006.","productDescription":"viii, 80 p.","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-098400","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":361283,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1006/ofr20191006.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1006"},{"id":361282,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1006/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.10273742675781,\n 42.22750046697999\n ],\n [\n -121.79374694824219,\n 42.22750046697999\n ],\n [\n -121.79374694824219,\n 42.595554553719204\n ],\n [\n -122.10273742675781,\n 42.595554553719204\n ],\n [\n -122.10273742675781,\n 42.22750046697999\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The morphodynamics of river-dominated deltas are largely controlled by the supply and retention of sediment within deltaic wetlands and the rate of relative sea-level rise. Yet, sediment budgets for deltas are often poorly constrained. In the Mississippi River Delta, a system rapidly losing land due to natural and anthropogenic causes, restoration efforts seek to build new land through the use of river diversions. At the Davis Pond Freshwater Diversion, a new crevasse splay has emerged since construction was completed in 2002. Here, we use beryllium-7 activity in sediment cores and USGS measurements of discharge and turbidity to calculate seasonal sediment input, deposition, and retention within the vegetated Davis Pond receiving basin. In winter/spring 2015, which included an experimental period of high discharge through the diversion, Davis Pond received 106,800 metric tons of sediment, 44% of which was retained within the basin. During this time, mean flow velocity was 0.21 m s−1 and mean turbidity was 56 formazin nephelometric units (FNU). In summer/fall 2015, the Davis Pond basin received 35,900 metric tons of sediment, 81% of which was retained. Mean flow velocity in summer/fall was 0.10 m s−1 and mean turbidity was 55 FNU. The increase in sediment retention from winter/spring 2015 to summer/fall 2015 may be due in part to the corresponding drop in water flow velocity, which allowed more sediment to settle out of suspension. Although high water discharge increases sediment input and deposition, increased turbulence associated with higher current velocity appears to increase sediment throughput and thereby decrease the sediment trapping efficiency. Sediment retention in Davis Pond is on the high end of the range seen in deltaic wetlands, perhaps due to the enclosed geometry of the receiving basin. Future diversion design and operation should target moderate water discharge and flow velocities in order to jointly maximize sediment deposition and retention and provide optimal conditions for delta growth.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2019.02.008","usgsCitation":"Keogh, M.E., Kolker, A.S., Snedden, G., and Renfro, A.A., 2019, Hydrodynamic controls on sediment retention in an emerging diversion-fed delta: Geomorphology, v. 332, p. 100-111, https://doi.org/10.1016/j.geomorph.2019.02.008.","productDescription":"12 p.","startPage":"100","endPage":"111","ipdsId":"IP-092068","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467886,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2019.02.008","text":"Publisher Index Page"},{"id":437565,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V7N49P","text":"USGS data release","linkHelpText":"Mineral content, bulk density, and beryllium-7 activity of wetland soils of the Davis Pond Freshwater Diversion Outfall Area, Louisiana, in 2015"},{"id":361386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Mississippi River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.36529541015625,\n 29.844217466091493\n ],\n [\n -90.2362060546875,\n 29.844217466091493\n ],\n [\n -90.2362060546875,\n 29.963857983730453\n ],\n [\n -90.36529541015625,\n 29.963857983730453\n ],\n [\n -90.36529541015625,\n 29.844217466091493\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"332","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Keogh, Molly E.","contributorId":213408,"corporation":false,"usgs":false,"family":"Keogh","given":"Molly","email":"","middleInitial":"E.","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":757660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolker, Alexander S.","contributorId":213409,"corporation":false,"usgs":false,"family":"Kolker","given":"Alexander","email":"","middleInitial":"S.","affiliations":[{"id":38749,"text":"Tulane University; Louisiana Universities Marine Consortium","active":true,"usgs":false}],"preferred":false,"id":757661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snedden, Gregg A. 0000-0001-7821-3709","orcid":"https://orcid.org/0000-0001-7821-3709","contributorId":212275,"corporation":false,"usgs":true,"family":"Snedden","given":"Gregg","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":757659,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Renfro, Alisha A.","contributorId":213410,"corporation":false,"usgs":false,"family":"Renfro","given":"Alisha","email":"","middleInitial":"A.","affiliations":[{"id":38750,"text":"National Wildlife Federation, Mississippi River Delta Campaign","active":true,"usgs":false}],"preferred":false,"id":757662,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202282,"text":"70202282 - 2019 - Dynamic N-mixture models with temporal variability in detection probability","interactions":[],"lastModifiedDate":"2019-02-20T10:44:50","indexId":"70202282","displayToPublicDate":"2019-02-20T10:44:45","publicationYear":"2019","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":"Dynamic N-mixture models with temporal variability in detection probability","docAbstract":"In theory parameters of dynamic N-mixture models can be estimated with multiple years of data without the robust design under the assumption of constant detection probability. However, such an assumption can rarely be met in long-term studies, and the consequences of violating this assumption in the inferences of dynamic N-mixture models have not been assessed. In this study we used simulation studies to evaluate inferences of the original dynamic N-mixture model and two of its spatial extensions in the face of temporal variability in detection probability. We first evaluated the dynamic N-mixture models when detection probability that varied temporally was wrongly treated as a constant. We then evaluated if the robust design was necessary for dynamic N-mixture models to provide valid parameter estimates when detection probability was correctly assumed to vary temporally. Our results showed that, when detection probability that varied temporally was wrongly treated as a constant, biases were introduced in the parameter estimates of dynamic N-mixture models. When detection probability was correctly assumed to vary temporally, the models could provide valid parameter estimates with the robust design. The model could also provide valid parameter estimates when detection probability was a random effect, even without the robust design. Based on our results, we strongly recommended considering temporal variability in detection probability when using dynamic N-mixture models to analyze long-term data and adopting the robust design in long-term surveys. Our work here is not only useful for data analysis but also important for research design, and thus are relevant to a wide range of studies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2018.12.007","usgsCitation":"Zhao, Q., and Royle, J.A., 2019, Dynamic N-mixture models with temporal variability in detection probability: Ecological Modelling, v. 393, p. 20-24, https://doi.org/10.1016/j.ecolmodel.2018.12.007.","productDescription":"5 p.","startPage":"20","endPage":"24","ipdsId":"IP-103124","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":361376,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"393","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhao, Qing","contributorId":213406,"corporation":false,"usgs":false,"family":"Zhao","given":"Qing","email":"","affiliations":[{"id":34045,"text":"Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO 80523","active":true,"usgs":false}],"preferred":false,"id":757623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":757622,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223367,"text":"70223367 - 2019 - Socioecological determinants of drought impacts and coping strategies for ranching operations in the Great Plains","interactions":[],"lastModifiedDate":"2021-08-25T13:24:49.325426","indexId":"70223367","displayToPublicDate":"2019-02-20T08:21:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Socioecological determinants of drought impacts and coping strategies for ranching operations in the Great Plains","docAbstract":"In Great Plains rangelands, drought is a recurring disturbance. Ranchers in this region expect to encounter drought but may not be adequately prepared for it. Efforts to encourage drought preparedness would benefit from a better understanding of the conditions under which managers make decisions to minimize the impacts of drought. We tested the direct and moderating roles of the drought hazard and the social-ecological context on drought impacts and response. This study was conducted with ranchers in western and central South Dakota and Nebraska following the drought that began in 2012. We surveyed ranchers regarding the effects of the drought and their responses and used multimodel analysis to explore the relationships among measures of drought preparedness, drought response, and drought impacts. Drought severity was the primary predictor of all impacts, but specific types of impacts were varied depending on the operation’s enterprise mix, resources, and management. The socioecological characteristics of the ranch system predicted drought response actions taken, by either providing the necessary resources and capacity to take action or creating sensitivity in the system that required action to be taken. We conclude with recommendations for learning from current drought experiences in order to better adapt to future drought events.
Southern New England provides key wintering habitat for White-winged Scoters (Melanitta fusca). This area has also pioneered the development of offshore wind energy in North America and the U.S. Bureau of Ocean Energy Management (BOEM) has established nine Wind Energy Area (WEA) lease blocks along the Atlantic Outer Continental Shelf in areas that may provide important staging and wintering habitat for scoters and other species of sea ducks. Concern over the potential impact of offshore wind energy on sea duck populations has led to efforts to develop models to understand their distribution, habitat use and site fidelity. We used satellite telemetry to document winter phenology and site fidelity, as well as fine-scale resource selection and habitat use of 40 White-winged Scoters along the southern New England continental shelf. Scoters spent over half of the annual cycle on the wintering grounds and demonstrated a high degree of inter-annual site fidelity to composite core-use areas. Sizes of individual 50% core-use home ranges were variable (x̅ = 868 km2; range = 32 to 4,220 km2) and individual 95% utilization distributions ranged widely (x̅ = 4,388 km2; range = 272 to 18,235 km2). More than half of all tagged birds occupied two or more discrete core-use areas that were up to 400 km apart. Throughout the study area, scoters selected for areas with lower salinity, lower sea surface temperature, higher chlorophyll-a concentrations, and higher hard-bottom substrate probability. Resource selection function models classified 18,649 km2 (23%) of the study area as high probability of use, which included or immediately bordered ~420 km2 of proposed WEA lease blocks. Future offshore wind energy developments in the region should avoid key habitats highlighted by this study and carefully consider the environmental characteristics selected by sea ducks when planning and siting future WEAs.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duy014","usgsCitation":"Meattey, D.E., McWilliams, S.R., Paton, P.W., Lepage, C., Gilliland, S.G., Savoy, L., Olsen, G.H., and Osenkowski, J.E., 2019, Resource selection and wintering phenology of White-winged Scoters in southern New England: Implications for offshore wind energy development: Condor, v. 121, no. 1, duy014, https://doi.org/10.1093/condor/duy014.","productDescription":"duy014","ipdsId":"IP-090294","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":488696,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/nrs_facpubs/529","text":"External Repository"},{"id":375459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Southern New England","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.3447265625,\n 40.84706035607122\n ],\n [\n -69.9169921875,\n 40.84706035607122\n ],\n [\n -69.9169921875,\n 43.866218006556394\n ],\n [\n -73.3447265625,\n 43.866218006556394\n ],\n [\n -73.3447265625,\n 40.84706035607122\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Meattey, Dustin E.","contributorId":225141,"corporation":false,"usgs":false,"family":"Meattey","given":"Dustin","email":"","middleInitial":"E.","affiliations":[{"id":41045,"text":"Department of Natural Resources Sciences, University of Rhode Island, Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":790515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McWilliams, Scott R.","contributorId":172328,"corporation":false,"usgs":false,"family":"McWilliams","given":"Scott","email":"","middleInitial":"R.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":790516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paton, Peter W.C.","contributorId":225142,"corporation":false,"usgs":false,"family":"Paton","given":"Peter","email":"","middleInitial":"W.C.","affiliations":[{"id":41045,"text":"Department of Natural Resources Sciences, University of Rhode Island, Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":790517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lepage, Christine","contributorId":194564,"corporation":false,"usgs":false,"family":"Lepage","given":"Christine","email":"","affiliations":[],"preferred":false,"id":790518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gilliland, Scott G.","contributorId":225143,"corporation":false,"usgs":false,"family":"Gilliland","given":"Scott","email":"","middleInitial":"G.","affiliations":[{"id":41046,"text":"Canadian Wildlife Service, Environment and Climate Change Canada, Sackville, NB","active":true,"usgs":false}],"preferred":false,"id":790519,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Savoy, Lucas","contributorId":171896,"corporation":false,"usgs":false,"family":"Savoy","given":"Lucas","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":790612,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olsen, Glenn H. 0000-0002-7188-6203 golsen@usgs.gov","orcid":"https://orcid.org/0000-0002-7188-6203","contributorId":40918,"corporation":false,"usgs":true,"family":"Olsen","given":"Glenn","email":"golsen@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":790520,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Osenkowski, Jason E.","contributorId":225144,"corporation":false,"usgs":false,"family":"Osenkowski","given":"Jason","email":"","middleInitial":"E.","affiliations":[{"id":41047,"text":"Rhode Island Department of Environmental Management, West Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":790521,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202058,"text":"tm7C21 - 2019 - User’s guide for Assessment Tract Aggregation GUI (ATA GUI)—A graphical user interface for the AggtEx.fn R script","interactions":[],"lastModifiedDate":"2019-03-12T11:01:57","indexId":"tm7C21","displayToPublicDate":"2019-02-19T15:15:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C21","displayTitle":"User’s Guide for Assessment Tract Aggregation GUI (ATA GUI)—A Graphical User Interface for the AggtEx.fn R Script","title":"User’s guide for Assessment Tract Aggregation GUI (ATA GUI)—A graphical user interface for the AggtEx.fn R script","docAbstract":"The U.S. Geological Survey three-part method for mineral resource assessments estimates numbers of undiscovered mineral deposits as probability distributions in geologically defined regions termed “permissive tracts.” This report describes a graphical user interface (GUI) script developed in open-source statistical software (R) that aggregates estimated undiscovered deposits of a given type from two or more permissive tracts using the AggtEx.fn R script. The AggtEx.fn R script aggregates undiscovered deposit estimates assuming independence, total dependence, or some degree of correlation among aggregated areas, given a user-specified correlation matrix. The script outputs three sets of aggregated estimates based on those three assumptions.
The GUI script described in this report, Assessment Tract Aggregation GUI (ATA GUI), provides an easy-to-use tool that supports implementation of the AggtEx.fn R script, installation of the R packages needed to run the application, and creation of a combined input file from individual files generated by the MapMark4GUI software. Users can also use EMINERS output information by creating a file of output values following the MapMark4GUI output file format. The probabilistic estimates of aggregated undiscovered deposits produced by ATA GUI can be used as input for MapMark4GUI to estimate contained resources for the aggregated tracts. MapMark4GUI uses Monte Carlo simulation to combine undiscovered deposit estimates with tonnage and grade models to simulate undiscovered mineral resources for a region of interest. This simulation includes the amounts of commodities and rock that could be present within a permissive tract. This report includes instructions on installing and running the ATA GUI script and describes the input and output files used and created during the aggregation process.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Computer programs in Book 7: Automated data processing and computations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C21","collaboration":" ","usgsCitation":"Shapiro, J.L., and Robinson, G.R., Jr., 2019, User’s guide for Assessment Tract Aggregation GUI (ATA GUI)—A graphical user interface for the AggtEx.fn R script: U.S. Geological Survey Techniques and Methods, book 7, chap. C21, 9 p., https://doi.org/10.3133/tm7c21.","productDescription":"Report: iv, 9 p.; Assessment Tract Aggregation GUI Package","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098557","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":361327,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c21/coverthb.jpg"},{"id":361328,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c21/tmc721.pdf","text":"Report","size":"1.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-C21"},{"id":361329,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c21/tm7c21_ATAGUI_Package.zip","text":"Assessment Tract Aggregation GUI Package","size":"751 KB","linkFileType":{"id":6,"text":"zip"}}],"contact":"Eastern Mineral and Environmental Resources Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
954 Mail Stop
Reston, VA 20192
In order to produce useful hydrologic and aquatic habitat data from the Landsat system, the U.S. Geological Survey has developed the “Dynamic Surface Water Extent” (DSWE) Landsat Science Product. DSWE will provide long-term, high-temporal resolution data on variations in inundation extent. The model used to generate DSWE is composed of five decision-rule based tests that do not require scene-based training. To allow its general application, required inputs are limited to the Landsat at-surface reflectance product and a digital elevation model. Unlike other Landsat-based water products, DSWE includes pixels that are only partially covered by water to increase inundation dynamics information content. Previously published DSWE model development included one wetland-focused test developed through visual inspection of field-collected Everglades spectra. A comparison of that test’s output against Everglades Depth Estimation Network (EDEN) in situ data confirmed the expectation that omission errors were a prime source of inaccuracy in vegetated environments. Further evaluation exposed a tendency toward commission error in coniferous forests. Improvements to the subpixel level “partial surface water” (PSW) component of DSWE was the focus of this research. Spectral mixture models were created from a variety of laboratory and image-derived endmembers. Based on the mixture modeling, a more “aggressive” PSW rule improved accuracy in herbaceous wetlands and reduced errors of commission elsewhere, while a second “conservative” test provides an alternative when commission errors must be minimized. Replication of the EDEN-based experiments using the revised PSW tests yielded a statistically significant increase in mean overall agreement (4%, p = 0.01, n = 50) and a statistically significant decrease (11%, p = 0.009, n = 50) in mean errors of omission. Because the developed spectral mixture models included image-derived vegetation endmembers and laboratory spectra for soil groups found across the US, simulations suggest where the revised DSWE PSW tests perform as they do in the Everglades and where they may prove problematic. Visual comparison of DSWE outputs with an unusual variety of coincidently collected images for locations spread throughout the US support conclusions drawn from Everglades quantitative analyses and highlight DSWE PSW component strengths and weaknesses.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11040374","usgsCitation":"Jones, J., 2019, Improved automated detection of subpixel-scale inundation – Revised Dynamic Surface Water Extent (DSWE) partial surface water tests: Remote Sensing, v. 11, no. 4, p. 1-26, https://doi.org/10.3390/rs11040374.","productDescription":"Article 374; 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-102379","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":467892,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11040374","text":"Publisher Index Page"},{"id":361339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, John 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":757437,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70202260,"text":"70202260 - 2019 - Estimating uncertainty of North American landbird population sizes","interactions":[],"lastModifiedDate":"2019-02-19T11:38:08","indexId":"70202260","displayToPublicDate":"2019-02-19T11:38:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating uncertainty of North American landbird population sizes","docAbstract":"An important metric for many aspects of species conservation planning and risk assessment is an estimate of total population size. For landbirds breeding in North America, Partners in Flight (PIF) generates global, continental, and regional population size estimates. These estimates are an important component of the PIF species assessment process, but have also been used by others for a range of applications. The PIF population size estimates are primarily calculated using a formula designed to extrapolate bird counts recorded by the North American Breeding Bird Survey (BBS) to regional population estimates. The extrapolation formula includes multiple assumptions and sources of uncertainty, but there were previously no attempts to quantify this uncertainty in the published population size estimates aside from a categorical data quality score. Using a Monte Carlo approach, we propagated the main sources of uncertainty arising from individual components of the model through to the final estimation of landbird population sizes. This approach results in distributions of population size estimates rather than point estimates. We found the width of uncertainty of population size estimates to be generally narrower than the order-of-magnitude distances between the population size score categories PIF uses in the species assessment process, suggesting confidence in the categorical ranking used by PIF. Our approach provides a means to identify species whose uncertainty bounds span more than one categorical rank, which was not previously possible with the data quality scores. Although there is still room for additional improvements to the estimation of avian population sizes and uncertainty, particularly with respect to replacing categorical model components with empirical estimates, our estimates of population size distributions have broader utility to a range of conservation planning and risk assessment activities relying on avian population size estimates.
","language":"English","publisher":"American Ornithological Society","doi":"10.5751/ACE-01331-140104","usgsCitation":"Stanton, J.C., Blancher, P.J., Rosenberg, K.V., Panjabi, A.O., and Thogmartin, W.E., 2019, Estimating uncertainty of North American landbird population sizes: Avian Conservation and Ecology, v. 14, no. 1, Article 4; 16 p., https://doi.org/10.5751/ACE-01331-140104.","productDescription":"Article 4; 16 p.","ipdsId":"IP-090781","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":467893,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-01331-140104","text":"Publisher Index Page"},{"id":437569,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90SWVFU","text":"USGS data release","linkHelpText":"Population Size uncertainty estimates"},{"id":361335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757536,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blancher, Peter J.","contributorId":175182,"corporation":false,"usgs":false,"family":"Blancher","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":757537,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberg, Kenneth V.","contributorId":171463,"corporation":false,"usgs":false,"family":"Rosenberg","given":"Kenneth","email":"","middleInitial":"V.","affiliations":[{"id":27615,"text":"Cornell Lab of Ornithology, Conservation Science Program","active":true,"usgs":false}],"preferred":false,"id":757538,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Panjabi, Arvind O.","contributorId":169967,"corporation":false,"usgs":false,"family":"Panjabi","given":"Arvind","email":"","middleInitial":"O.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":757539,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":757540,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70200151,"text":"sir20185125 - 2019 - Potential for increased inundation in flood-prone regions of southeast Florida in response to climate and sea-level changes in Broward County, Florida, 2060–69","interactions":[],"lastModifiedDate":"2019-02-19T14:54:42","indexId":"sir20185125","displayToPublicDate":"2019-02-19T11:28:48","publicationYear":"2019","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":"2018-5125","displayTitle":"Potential for Increased Inundation in Flood-Prone Regions of Southeast Florida in Response to Climate and Sea-Level Changes in Broward County, Florida, 2060–69","title":"Potential for increased inundation in flood-prone regions of southeast Florida in response to climate and sea-level changes in Broward County, Florida, 2060–69","docAbstract":"The U.S. Geological Survey, in cooperation with Broward County Environmental Planning and Resilience Division, has developed county-scale and local-scale groundwater/surface-water models to study the potential for increased inundation and flooding in eastern Broward County that are due to changes in future climate and sea-level rise. These models were constructed by using MODFLOW 2005, with the surface-water system represented by using the Surface-Water Routing process and a new Urban Runoff process. The local-scale model allowed the use of finer grid resolution in a selected area of the county, whereas the county-scale model provided boundary conditions for the local-scale model and insight into the hydrologic behavior of the larger system. The aquifer layering, properties, and boundaries relied heavily on a previous three-dimensional variable-density solute-transport model of the same area developed by the U.S. Geological Survey. The surface-water system within these new models actively simulates a part of the extensive canal network by using level-pool routing and active structure operations within the Surface-Water Routing process. These models were used to simulate a historical base-case period (1990–99) by using measured data and regional climate model rainfall and potential evapotranspiration output. The simulated flow and water-level results generally captured the behavior of the hydrologic system. A future period (2060–69) was simulated by using regional climate model rainfall and potential evapotranspiration output representing a wetter and drier future and low, intermediate, and high sea-level rise projections. The results were used to evaluate the potential effects on the surface-water drainage system, coastal-structure operation, and wet-season groundwater levels.
Future period simulations using the county-scale model indicate that (1) the effects of the changing climate and sea level are much more evident in eastern and coastal areas of Broward County compared to western areas, with increases in groundwater level nearly equivalent to sea-level rise; (2) coastal groundwater-level increases are distributed farther inland in the wetter future scenarios than in the drier future scenarios; (3) water levels at the westernmost groundwater station locations exhibited little change caused by sea-level rise and showed more dependence on changes in precipitation; (4) there was a reduced west-to-east groundwater gradient with increasing sea-level rise; and (5) increased downstream tidal stage at the S–13 structure resulted in increased reliance on the pump to control upstream inland canal stages. Future simulations using the local-scale model indicate similar behavior as seen in the county-scale model: (1) the coastal areas exhibited the largest impacts in groundwater levels in the future scenarios; (2) the westernmost, interior areas exhibited little change during the future scenarios; and (3) there was an increased reliance on the pump at the S–13 coastal structure but to a lesser extent than indicated in the county-scale model because of the reduced temporal scale of the local-scale model.
Possible adaptation and mitigation strategies were simulated to evaluate the county-scale and local-scale models’ ability to simulate hydrologic changes. Alterations to S–13 pump operations within the county-scale model were tested, and results indicate a reduced effect of sea-level rise inland of the control structure, but the affected area is spatially limited. The concept of using pumps to reduce the local groundwater levels in two neighborhood-sized areas was tested by using the local-scale model. The MODFLOW 2005 Drain package was used to remove groundwater by using drainage elevations set to zero, 1 foot, and 2 feet above average wet-season groundwater levels. Area 1 was well connected to coastal boundaries, and a high rate of groundwater removal was required, whereas the rate of groundwater removal required was greatly reduced in Area 2, which is less connected to tidal boundaries. Water for these scenarios was assumed to be pumped to tide with no downstream effects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185125","collaboration":"Prepared in cooperation with the Broward County Environmental Planning and Resilience Division","usgsCitation":"Decker, J.D., Hughes, J.D., and Swain, E.D., 2019, Potential for increased inundation in flood-prone regions of southeast Florida in response to climate and sea-level changes in Broward County, Florida, 2060–69: U.S. Geological Survey Scientific Investigations Report 2018–5125, 106 p., https://doi.org/10.3133/sir20185125.","productDescription":"Report: viii, 106 p.; Data Release","numberOfPages":"118","onlineOnly":"Y","ipdsId":"IP-066244","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":361163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5125/sir20185125.pdf","text":"Report","size":"10.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5125"},{"id":361162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5125/coverthb.jpg"},{"id":361164,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E6INWZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW 2005 data sets for the simulation of potential increased inundation in flood-prone regions of Southeast Florida in response to climate and sea-level changes in Broward County, Florida, 2060–69"}],"country":"United States","state":"Florida","county":"Broward County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.44326782226562,\n 25.95557515483912\n ],\n [\n -80.07522583007812,\n 25.95557515483912\n ],\n [\n -80.07522583007812,\n 26.331576128197028\n ],\n [\n -80.44326782226562,\n 26.331576128197028\n ],\n [\n -80.44326782226562,\n 25.95557515483912\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Aquatic features critical to watershed hydrology range widely in size from narrow, shallow streams to large, deep lakes. In this study we evaluated wetland, lake, and river systems across the Prairie Pothole Region to explore where pan-sharpened high-resolution (PSHR) imagery, relative to Landsat imagery, could provide additional data on surface water distribution and movement, missed by Landsat. We used the monthly Global Surface Water (GSW) Landsat product as well as surface water derived from Landsat imagery using a matched filtering algorithm (MF Landsat) to help consider how including partially inundated Landsat pixels as water influenced our findings. The PSHR outputs (and MF Landsat) were able to identify ~60–90% more surface water interactions between waterbodies, relative to the GSW Landsat product. However, regardless of Landsat source, by documenting many smaller (<0.2 ha), inundated wetlands, the PSHR outputs modified our interpretation of wetland size distribution across the Prairie Pothole Region.
The General Lake Model (GLM) is a one-dimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of researchers using sensors to understand lake functioning and address questions about how lakes around the world respond to climate and land use change. The scale and diversity of lake types, locations, and sizes, and the expanding observational datasets created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management questions relevant to the GLEON community. This paper summarizes the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice cover dynamics, vertical mixing, and inflow–outflow dynamics. We demonstrate the suitability of the model for different lake types that vary substantially in their morphology, hydrology, and climatic conditions. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated simulations of water quality and ecosystem health, and options for integration with other environmental models are outlined. Finally, we discuss utilities for the analysis of model outputs and uncertainty assessments, model operation within a distributed cloud-computing environment, and as a tool to support the learning of network participants.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-12-473-2019","usgsCitation":"Hipsey, M.R., Bruce, L.C., Boon, C., Busch, B., Carey, C.C., Hamilton, D., Hanson, P.C., Read, J.S., de Sousa, E., Weber, M., and Winslow, L., 2019, A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON): Geoscientific Model Development, v. 12, p. 473-523, https://doi.org/10.5194/gmd-12-473-2019.","productDescription":"51 p.","startPage":"473","endPage":"523","ipdsId":"IP-091920","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":467899,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-12-473-2019","text":"Publisher Index Page"},{"id":361294,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Hipsey, Matthew R.","contributorId":213314,"corporation":false,"usgs":false,"family":"Hipsey","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":38735,"text":"UWA School of Agriculture & Environment, The University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":757423,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bruce, Louise C.","contributorId":131100,"corporation":false,"usgs":false,"family":"Bruce","given":"Louise","email":"","middleInitial":"C.","affiliations":[{"id":7243,"text":"School of Earth & Environment, The University of Western Australia, Perth, Australia","active":true,"usgs":false}],"preferred":false,"id":757424,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boon, Casper","contributorId":213315,"corporation":false,"usgs":false,"family":"Boon","given":"Casper","email":"","affiliations":[{"id":38735,"text":"UWA School of Agriculture & Environment, The University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":757425,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Busch, Brendan","contributorId":213316,"corporation":false,"usgs":false,"family":"Busch","given":"Brendan","email":"","affiliations":[{"id":38735,"text":"UWA School of Agriculture & Environment, The University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":757426,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carey, Cayelan C.","contributorId":130969,"corporation":false,"usgs":false,"family":"Carey","given":"Cayelan","email":"","middleInitial":"C.","affiliations":[{"id":7185,"text":"Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":757427,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hamilton, David P.","contributorId":166840,"corporation":false,"usgs":false,"family":"Hamilton","given":"David P.","affiliations":[{"id":24543,"text":"Environmental Research Institute, University of Waikato, Private Bag 3015, Hamilton 3240, New Zealand.","active":true,"usgs":false}],"preferred":false,"id":757428,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hanson, Paul C.","contributorId":35634,"corporation":false,"usgs":false,"family":"Hanson","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":757429,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":757422,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"de Sousa, Eduardo","contributorId":213317,"corporation":false,"usgs":false,"family":"de Sousa","given":"Eduardo","email":"","affiliations":[{"id":38735,"text":"UWA School of Agriculture & Environment, The University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":757430,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weber, Michael","contributorId":213318,"corporation":false,"usgs":false,"family":"Weber","given":"Michael","affiliations":[],"preferred":false,"id":757431,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Winslow, Luke A. 0000-0002-8602-5510","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":211187,"corporation":false,"usgs":false,"family":"Winslow","given":"Luke A.","affiliations":[{"id":12656,"text":"Rensselaer Polytechnic Institute","active":true,"usgs":false}],"preferred":false,"id":757432,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70202737,"text":"70202737 - 2019 - Human-induced and natural carbon storage in floodplains of the Central Valley of California","interactions":[],"lastModifiedDate":"2019-03-25T09:16:40","indexId":"70202737","displayToPublicDate":"2019-02-15T10:56:13","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Human-induced and natural carbon storage in floodplains of the Central Valley of California","docAbstract":"Active floodplains can putatively store large amounts of organic carbon (SOC) in subsoils originating from catchment erosion processes with subsequent floodplain deposition. Our study focussed on the assessment of SOC pools associated with alluvial floodplain soils that are affected by human-induced changes in floodplain deposition and in situ SOC mineralisation due to land use change and drainage. We evaluated depth-dependent SOC contents based on 23 soil cores down to 3 m and 10 drillings down to 7 m in a floodplain area of the lower Cosumnes River. An estimate of 266 Mg C ha−1 or about 59% of the entire SOC stored within the 7 m profiles was found in the upper 2 m. Most profiles (n = 25) contained discrete buried A horizons at depths of approximately 0.8 m. These profiles had up to 130% higher SOC stocks. The mean δ13C of all deep soil profiles clearly indicated that arable land use has already altered the stable isotopic signature in the first meter of the profile. Radiocarbon dating showed that the 14C age in the buried horizon was younger than in overlaying soils indicating a substantial sedimentation phase for the overlaying soils. An additional analysis of total mercury contents in the soil profiles indicated that this sedimentation was associated with upstream hydraulic gold mining after the 1850s. In summary, deep alluvial soils in floodplains store large amounts of SOC not yet accounted for in global carbon models. Historic data give evidence that large amounts of sediment were transported into the floodplains of most rivers of the Central Valley and deposited over organically rich topsoil, which promoted the stabilization of SOC, and needs to be considered to improve our understanding of the human-induced interference with C cycling.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2018.09.205","usgsCitation":"Steger, K., Fiener, P., Marvin-DiPasquale, M.C., Viers, J.H., and Smart, D.R., 2019, Human-induced and natural carbon storage in floodplains of the Central Valley of California: Science of the Total Environment, v. 651, no. 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The goal of the NESQA was to assess the health of wadeable streams in the region by characterizing multiple water-quality factors that are stressors to aquatic life and by evaluating the relation between these stressors and the condition of biological communities. Urbanization, agriculture, and human modifications to streamflow are anthropogenic changes that greatly affect water quality in the region; consequently, the study design primarily selected sites and targeted stressors associated with these activities. The NESQA built on a prior NAWQA study conducted in the region in 2014, the Atlantic Highlands flow-ecology study, which investigated the effects of anthropogenically modified flows on aquatic biological communities in primarily forested watersheds. Land-cover data for the NESQA were used to identify and select sites within the region that had watersheds ranging in levels of urban and agricultural development. A total of 95 sites were selected: 67 on streams in watersheds representing a range of urban land use, 13 on streams in watersheds with some degree of agricultural land use, and 15 on streams in predominantly forested watersheds with little development. Depending on land-cover characteristics, sites were sampled weekly for metal and organic contaminants, nutrients, and sediment for either a 9-week period that began the week of June 6, 2016, or a 4-week period that begin the week of July 11, 2016. Beginning August 1, 2016, and for about 2 weeks, an ecological survey was conducted at every site to assess stream habitat, and algal, benthic invertebrate, and fish communities. Additional samples collected during the ecological surveys were streambed sediment for chemical analysis and toxicity testing, and fish tissue for mercury analysis. This report describes the various study components and methods of the NESQA and describes a precursor effort for the Atlantic Highlands flow-ecology study. Details are presented for measurements of water quality, sediment chemistry, streamflow, and ecological surveys of stream biota and habitat, as well as processes of sample analysis, quality assurance and quality control, and data management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181183","collaboration":"National Water Quality Program","usgsCitation":"Coles, J.F., Riva-Murray, K., Van Metre, P.C., Button, D.T., Bell, A.H., Qi, S.L., Journey, C.A., and Sheibley, R.W., 2019, Design and methods of the U.S. Geological Survey Northeast Stream Quality Assessment (NESQA), 2016: U.S. Geological Survey Open-File Report 2018–1183, 46 p., https://doi.org/10.3133/ofr20181183.","productDescription":"Report: vii, 46 p.; Appendixes 1 and 2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-095438","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":361093,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1183/ofr20181183_appendix2.xlsx","text":"Appendix 2, tables 2.1 through 2.10: Excel ","size":"119 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":361094,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1183/ofr20181183_appendixes.zip","text":"Appendixes 1 and 2, all tables in CSV format","size":"5.45 GB","linkFileType":{"id":6,"text":"zip"}},{"id":361095,"rank":6,"type":{"id":18,"text":"Project Site"},"url":"https://webapps.usgs.gov/rsqa/#!/"},{"id":361090,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1183/coverthb.jpg"},{"id":361091,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1183/ofr20181183.pdf","text":"Report","size":"2.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1183"},{"id":361092,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1183/ofr20181183_appendix1.xlsx","text":"Appendix 1, tables 1.1 through 1.4: Excel","size":"777 KB","linkFileType":{"id":3,"text":"xlsx"}}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.9365234375,\n 40.17887331434696\n ],\n [\n -68.291015625,\n 40.17887331434696\n ],\n [\n -68.291015625,\n 47.60616304386874\n ],\n [\n -79.9365234375,\n 47.60616304386874\n ],\n [\n -79.9365234375,\n 40.17887331434696\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The effectiveness of Integrated Water Resource Management (IWRM) modeling hinges on the quality of practices employed through the process, starting from early problem definition all the way through to using the model in a way that serves its intended purpose. The adoption and implementation of effective modeling practices need to be guided by a practical understanding of the variety of decisions that modelers make, and the information considered in making these choices. There is still limited documented knowledge on the modeling workflow, and the role of contextual factors in determining this workflow and which practices to employ. This paper attempts to contribute to this knowledge gap by providing systematic guidance of the modeling practices through the phases (Planning, Development, Application, and Perpetuation) and steps that comprise the modeling process, positing questions that should be addressed. Practice-focused guidance helps explain the detailed process of conducting IWRM modeling, including the role of contextual factors in shaping practices. We draw on findings from literature and the authors’ collective experience to articulate what and how contextual factors play out in employing those practices. In order to accelerate our learning about how to improve IWRM modeling, the paper concludes with five key areas for future practice-related research: knowledge sharing, overcoming data limitations, informed stakeholder involvement, social equity and uncertainty management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2019.02.013","usgsCitation":"Badham, J., Elsawah, S., Guillaume, J., Hamilton, S.H., Hunt, R., Jakeman, A.J., Pierce, S.A., Babbar-Sebens, M., Fu, B., Gober, P., Hill, M.C., Iwanaga, T., Loucks, D.P., Merritt, W.S., Peckham, S.D., Richmond, A.K., Zare, F., Ames, D.P., and Bammer, G., 2019, Effective modeling for Integrated Water Resource Management: A guide to contextual practices by phases and steps and future opportunities: Environmental Modelling & Software, v. 116, 17 p., https://doi.org/10.1016/j.envsoft.2019.02.013.","productDescription":"17 p.","ipdsId":"IP-098737","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":467903,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://ro.ecu.edu.au/ecuworkspost2013/5935","text":"Publisher Index Page"},{"id":383145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"116","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Badham, J.","contributorId":248842,"corporation":false,"usgs":false,"family":"Badham","given":"J.","affiliations":[{"id":36943,"text":"Queens University","active":true,"usgs":false}],"preferred":false,"id":810046,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elsawah, Sondoss","contributorId":146686,"corporation":false,"usgs":false,"family":"Elsawah","given":"Sondoss","affiliations":[],"preferred":false,"id":810047,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guillaume, Joseph H. 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Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Simulating demography, genetics, and spatially explicit processes to inform reintroduction of a threatened char","docAbstract":"The success of species reintroductions can depend on a combination of environmental, demographic, and genetic factors. Although the importance of these factors in the success of reintroductions is well‐accepted, they are typically evaluated independently, which can miss important interactions. For species that persist in metapopulations, movement through and interaction with the landscape is predicted to be a vital component of persistence. Simulation‐based approaches are a promising technique for evaluating the independent and combined effects of these factors on the outcome of various reintroduction and associated management actions. We report results from a simulation study of bull trout (Salvelinus confluentus) reintroduction to three watersheds of the Pend Oreille River system in northeastern Washington State, USA. We used an individual‐based, spatially explicit simulation model to evaluate how reintroduction strategies, life history variation, and riverscape structure (e.g., network topology) interact to influence the demographic and genetic characteristics of reintroduced bull trout populations in three watersheds. Simulation scenarios included a range of initial genetic stocks (informed by empirical bull trout genetic data), variation in migratory tendency and life history, and two landscape connectivity alternatives representing a connected network (isolation‐by‐distance) and a fragmented network (isolation‐by‐barrier, using the known existing barriers). A novel feature of these simulations was the ability to consider the interaction of both demographic and genetic (i.e., demogenetic) factors in riverscapes with implicit asymmetric movement probabilities across the barriers. We found that connectivity (presence or absence of barriers) had the largest effect on demographic and genetic outcomes over 200 yr, with a greater effect than both initial genetic diversity and life history variation. We also identified regions of the study system in which bull trout populations persisted across a wide range of demographic, life history, and environmental connectivity parameters. Finally, we found no evidence that initial neutral genetic diversity influenced genetic diversity and structure after 200 yr; instead, genetic drift due to stray rate and population isolation dominated and erased any initial differences in genetic diversity. Our results highlight the utility of spatially explicit demogenetic approaches in exploring and understanding population dynamics—and their implications for management strategies—in fresh waters.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2589","usgsCitation":"Mims, M.C., Day, C.C., Burkhart, J.J., Fuller, M.R., Hinkle, J., Bearlin, A., Dunham, J.B., DeHaan, P.W., Holden, Z.A., and Landguth, E.L., 2019, Simulating demography, genetics, and spatially explicit processes to inform reintroduction of a threatened char: Ecosphere, v. 10, no. 2, p. 1-24, https://doi.org/10.1002/ecs2.2589.","productDescription":"Article e02589; 24 p.","startPage":"1","endPage":"24","ipdsId":"IP-103940","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":467904,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2589","text":"Publisher Index Page"},{"id":361262,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Pend Oreille 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A.","contributorId":213263,"corporation":false,"usgs":false,"family":"Holden","given":"Zachary","email":"","middleInitial":"A.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":757291,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Landguth, Erin L.","contributorId":190821,"corporation":false,"usgs":false,"family":"Landguth","given":"Erin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":757292,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70202209,"text":"70202209 - 2019 - River‐valley morphology, basin size, and flow‐event magnitude interact to produce wide variation in flooding dynamics","interactions":[],"lastModifiedDate":"2019-02-14T12:37:40","indexId":"70202209","displayToPublicDate":"2019-02-14T12:37:37","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"River‐valley morphology, basin size, and flow‐event magnitude interact to produce wide variation in flooding dynamics","docAbstract":"Inundation dynamics are a key driver of ecosystem form and function in river‐valley bottoms. Inundation itself is an outcome of multi‐scalar interactions and can vary strongly within and among river reaches. As a result, establishing to what degree and how inundation dynamics vary spatially both within and among river reaches can be challenging. The objective of this study was to understand how river‐valley morphology, basin size, and flow‐event magnitude interact to affect inundation dynamics in river‐valley bottoms. We used 2D hydraulic models to simulate inundation in four river reaches from Maryland's Piedmont physiographic province, and qualitatively and quantitatively summarized within‐ and among‐reach patterns of inundation extent, duration, depth, shear stress, and wetting frequencies. On average, reaches from confined valley settings experienced less extensive flooding, shorter durations and shallower depths, stronger gradients of maximum shear stress, and relatively infrequent wetting compared to reaches from unconfined settings. These patterns were generally consistent across flow‐event magnitudes. Patterns of within‐reach flooding across event magnitudes revealed complex interactions between hydrology and surface topography. We concluded that valley morphology had a greater impact on flooding patterns than basin size: Inundation patterns were more consistent across reaches of similar morphology than similar basin size, but absolute values of inundation characteristics varied between large and small basins. Our results showed that the manifestation of out‐of‐bank flows in valley floors can vary widely depending on geomorphic context, even within a single physiographic province, which suggests that hydrologic and hydraulic conditions experienced on the valley floor may not be well represented by existing hydrologic metrics derived from discharge data alone. We thus support the notion that 2D hydraulic models can be useful hydrometric tools for cross‐scale investigations of floodplain ecosystems.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2546","usgsCitation":"Van Appledorn, M., Baker, M.E., and Miller, A.J., 2019, River‐valley morphology, basin size, and flow‐event magnitude interact to produce wide variation in flooding dynamics: Ecosphere, v. 10, no. 1, p. 1-25, https://doi.org/10.1002/ecs2.2546.","productDescription":"Article e02546; 25 p.","startPage":"1","endPage":"25","ipdsId":"IP-096187","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":467905,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2546","text":"Publisher Index Page"},{"id":437572,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ITQTNQ","text":"USGS data release","linkHelpText":"Complex interactions among river-valley morphology, basin size, and flow-event magnitude structure the physical template of floodplain ecosystems. Data"},{"id":361256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Chesapeake Bay Watershed","volume":"10","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757248,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baker, Matthew E.","contributorId":149189,"corporation":false,"usgs":false,"family":"Baker","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":17665,"text":"Department of Geography and Environmental Systems, University of Maryland, Baltimore County, Baltimore, Maryland, US","active":true,"usgs":false}],"preferred":false,"id":757249,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Andrew J.","contributorId":207595,"corporation":false,"usgs":false,"family":"Miller","given":"Andrew","email":"","middleInitial":"J.","affiliations":[{"id":15309,"text":"University of Maryland Baltimore County","active":true,"usgs":false}],"preferred":false,"id":757250,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202210,"text":"70202210 - 2019 - Effects of urban multi-stressors on three stream biotic assemblages","interactions":[],"lastModifiedDate":"2019-02-14T12:28:29","indexId":"70202210","displayToPublicDate":"2019-02-14T12:28:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Effects of urban multi-stressors on three stream biotic assemblages","docAbstract":"During 2014, the U.S. Geological Survey (USGS) National Water-Quality Assessment(NAWQA) project assessed stream quality in 75 streams across an urban disturbance gradient within the Piedmont ecoregion of southeastern United States. Our objectives were to identify primary instream stressors affecting algal, macroinvertebrate and fish assemblages in wadeable streams. Biotic communities were surveyed once at each site, and various instream stressors were measured during a 4-week index period preceding the ecological sampling. The measured stressors included nutrients; contaminants in water, passive samplers, and sediment; instream habitat; and flow variability. All nine boosted regression tree models – three for each of algae, invertebrates, and fish – had cross-validation R2 (CV R2) values of 0.41 or above, and an invertebrate model had the highest CV R2 of 0.65. At least one contaminant metric was important in every model, and minimum daytime dissolved oxygen (DO), nutrients, and flow alteration were important explanatory variables in many of the models. Physical habitat metrics such as sediment substrate were only moderately important. Flow alteration metrics were useful factors in eight of the nine models. Total phosphorus, acetanilide herbicides and flow (time since last peak) were important in all three algal models, whereas insecticide metrics (especially those representing fipronil and imidacloprid) were dominant in the invertebrate models. DO values below approximately 7 mg/L corresponded to a strong decrease in sensitive taxa or an increase in tolerant taxa. DO also showed strong interactions with other variables, particularly contaminants and sediment, where the combined effect of low DO and elevated contaminants increased the impact on the biota more than each variable individually. Contaminants and flow alteration were strongly correlated to urbanization, indicating the importance of urbanization to ecological stream condition in the region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2018.12.240","usgsCitation":"Waite, I.R., Munn, M., Moran, P.W., Konrad, C.P., Nowell, L.H., Meador, M.R., Van Metre, P.C., and Carlisle, D.M., 2019, Effects of urban multi-stressors on three stream biotic assemblages: Science of the Total Environment, v. 660, p. 1472-1485, https://doi.org/10.1016/j.scitotenv.2018.12.240.","productDescription":"14 p.","startPage":"1472","endPage":"1485","ipdsId":"IP-100484","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":467906,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2018.12.240","text":"Publisher Index Page"},{"id":437573,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L86OG8","text":"USGS data release","linkHelpText":"Water-quality and stream-habitat metrics calculated for the National Water-Quality Assessment Program's Regional Stream Quality Assessment conducted in the southeast United States in support of ecological and habitat stressor models, 2014"},{"id":361255,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"660","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Waite, Ian R. 0000-0003-1681-6955 iwaite@usgs.gov","orcid":"https://orcid.org/0000-0003-1681-6955","contributorId":616,"corporation":false,"usgs":true,"family":"Waite","given":"Ian","email":"iwaite@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757251,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munn, Mark D. 0000-0002-7154-7252","orcid":"https://orcid.org/0000-0002-7154-7252","contributorId":205360,"corporation":false,"usgs":true,"family":"Munn","given":"Mark D.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757252,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moran, Patrick W. 0000-0002-2002-3539 pwmoran@usgs.gov","orcid":"https://orcid.org/0000-0002-2002-3539","contributorId":489,"corporation":false,"usgs":true,"family":"Moran","given":"Patrick","email":"pwmoran@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757253,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Konrad, Christopher P. 0000-0002-7354-547X cpkonrad@usgs.gov","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":1716,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","email":"cpkonrad@usgs.gov","middleInitial":"P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nowell, Lisa H. 0000-0001-5417-7264 lhnowell@usgs.gov","orcid":"https://orcid.org/0000-0001-5417-7264","contributorId":490,"corporation":false,"usgs":true,"family":"Nowell","given":"Lisa","email":"lhnowell@usgs.gov","middleInitial":"H.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":757255,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meador, Michael R. 0000-0001-5956-3340 mrmeador@usgs.gov","orcid":"https://orcid.org/0000-0001-5956-3340","contributorId":195592,"corporation":false,"usgs":true,"family":"Meador","given":"Michael","email":"mrmeador@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":757256,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Van Metre, Peter C. 0000-0001-7564-9814","orcid":"https://orcid.org/0000-0001-7564-9814","contributorId":211144,"corporation":false,"usgs":true,"family":"Van Metre","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":757257,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":757258,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202191,"text":"70202191 - 2019 - Most Earth-surface calcites precipitate out of isotopic equilibrium","interactions":[],"lastModifiedDate":"2019-02-14T09:43:05","indexId":"70202191","displayToPublicDate":"2019-02-14T09:43:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Most Earth-surface calcites precipitate out of isotopic equilibrium","docAbstract":"Oxygen-isotope thermometry played a critical role in the rise of modern geochemistry and remains extensively used in (bio-)geoscience. Its theoretical foundations rest on the assumption that 18O/16O partitioning among water and carbonate minerals primarily reflects thermodynamic equilibrium. However, after decades of research, there is no consensus on the true equilibrium 18O/16O fractionation between calcite and water (18αcc/w). Here, we constrain the equilibrium relations linking temperature, 18αcc/w, and clumped isotopes (Δ47) based on the composition of extremely slow-growing calcites from Devils Hole and Laghetto Basso (Corchia Cave). Equilibrium 18αcc/w values are systematically ~1.5‰ greater than those in biogenic and synthetic calcite traditionally considered to approach oxygen-isotope equilibrium. We further demonstrate that subtle disequilibria also affect Δ47 in biogenic calcite. These observations provide evidence that most Earth-surface calcites fail to achieve isotopic equilibrium, highlighting the need to improve our quantitative understanding of non-equilibrium isotope fractionation effects instead of relying on phenomenological calibrations.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-019-08336-5","usgsCitation":"Daeron, M., Drysdale, R.N., Peral, M., Huyghe, D., Blamart, D., Coplen, T.B., Lartaud, F., and Zanchetta, G., 2019, Most Earth-surface calcites precipitate out of isotopic equilibrium: Nature Communications, v. 10, no. 1, p. 1-7, https://doi.org/10.1038/s41467-019-08336-5.","productDescription":"Article number: 429; 7 p.","startPage":"1","endPage":"7","ipdsId":"IP-097869","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":460475,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-019-08336-5","text":"Publisher Index Page"},{"id":361242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Daeron, Mathieu 0000-0003-1210-9786","orcid":"https://orcid.org/0000-0003-1210-9786","contributorId":213227,"corporation":false,"usgs":false,"family":"Daeron","given":"Mathieu","email":"","affiliations":[{"id":38725,"text":"Université Paris-Saclay, France","active":true,"usgs":false}],"preferred":false,"id":757161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drysdale, Russell N 0000-0001-7867-031X","orcid":"https://orcid.org/0000-0001-7867-031X","contributorId":213230,"corporation":false,"usgs":false,"family":"Drysdale","given":"Russell","email":"","middleInitial":"N","affiliations":[{"id":16747,"text":"University of Melbourne, Australia","active":true,"usgs":false}],"preferred":false,"id":757164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peral, Marion 0000-0002-6027-5482","orcid":"https://orcid.org/0000-0002-6027-5482","contributorId":213228,"corporation":false,"usgs":false,"family":"Peral","given":"Marion","email":"","affiliations":[{"id":38725,"text":"Université Paris-Saclay, France","active":true,"usgs":false}],"preferred":false,"id":757162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huyghe, Damien","contributorId":213229,"corporation":false,"usgs":false,"family":"Huyghe","given":"Damien","email":"","affiliations":[{"id":38726,"text":"Sorbonne Université, F-66650 Banyuls-sur-mer, France","active":true,"usgs":false}],"preferred":false,"id":757163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blamart, Dominique 0000-0003-1422-362X","orcid":"https://orcid.org/0000-0003-1422-362X","contributorId":213231,"corporation":false,"usgs":false,"family":"Blamart","given":"Dominique","email":"","affiliations":[{"id":38725,"text":"Université Paris-Saclay, France","active":true,"usgs":false}],"preferred":false,"id":757165,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":757160,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lartaud, Franck 0000-0001-7130-2944","orcid":"https://orcid.org/0000-0001-7130-2944","contributorId":213232,"corporation":false,"usgs":false,"family":"Lartaud","given":"Franck","email":"","affiliations":[{"id":38726,"text":"Sorbonne Université, F-66650 Banyuls-sur-mer, France","active":true,"usgs":false}],"preferred":false,"id":757166,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zanchetta, Giovanni 0000-0002-7080-9599","orcid":"https://orcid.org/0000-0002-7080-9599","contributorId":213233,"corporation":false,"usgs":false,"family":"Zanchetta","given":"Giovanni","email":"","affiliations":[{"id":38727,"text":"University of Pisa, Italy","active":true,"usgs":false}],"preferred":false,"id":757167,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70005455,"text":"tm11B2 - 2019 - US Topo Product Standard","interactions":[],"lastModifiedDate":"2019-02-14T10:58:54","indexId":"tm11B2","displayToPublicDate":"2019-02-13T15:30:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"11-B2","title":"US Topo Product Standard","docAbstract":"This document defines a U.S. Geological Survey (USGS) digital topographic map. This map product series, named “US Topo,” is modeled on the now historical USGS 7.5-minute (1:24,000 scale) topographic map series produced and printed by the USGS from 1947 to 2006. US Topo maps have the same extent, scale, and general layout as the historical topographic maps. US Topo maps incorporate an orthorectified image (hereinafter referred to as “orthoimage”) and shaded relief image along with a selection of data that were included in the historical 7.5-minute topographic maps. Between June and September of 2017, the USGS transitioned the format of US Topo maps to be published, by using a geospatial extension, in an International Organization for Standardization (ISO) 32000-compliant Adobe® portable document format (PDF) that is called a “geospatial PDF.” Previously, US Topo maps were published, by using geospatial extensions patented by TerraGo® Technologies, in PDF in a format called a “GeoPDF®.” The geospatial PDF design allows a user to zoom in and out in a georeferenced environment, turn layers on and off, view or print any combination of layers, and print any portion of the map at the published scale. US Topo maps are intended to serve conventional map users by providing geographic information system (GIS) information in symbolized form in the customary topographic map layout. The maps are not intended for advanced GIS analysis applications. These products are built on standard coordinate systems and include full U.S. National Grid (USNG) lines, making US Topo maps particularly useful for emergency first-response operations. These maps are also used by traditional topographic map users, such as resource managers, planners, and recreational users who continue to have a need for the symbolized feature data contained in the 7.5-minute quadrangle maps. Full-size style sheet templates in PDF defining the placement of map elements, marginalia, and font sizes and styles accompany this standard. US Topo maps published as geospatial PDFs are fashioned to conform to these style sheets so that a user can print out a map at the 1:24,000, 1:25,000, or 1:20,000 scale using the dimensions of the traditional standard 7.5-minute quadrangle. Symbology and type specifications for feature content and detailed requirements for geospatial content will be published separately. This document is an update of the US Topo Product Standard published in 2011 (Cooley and others, 2011). It is applicable to all US Topo maps. Updates in this version include
|
Director, National Geospatial Technical Operations Center
U.S. Geological Survey
Box 25046, MS 510
Denver Federal Center
Denver, CO 80225-0046
Recent coastal storms and associated recovery efforts have led to increased investment in nature-based coastal protection, including restoration of salt marshes and construction of living shorelines. In particular, many of these efforts focus on increasing vertical elevation through sediment nourishment, where sediment is removed from the tidal channel and placed on the marsh plain, or preventing lateral erosion through shoreline protection. In the USA alone, millions of dollars have been allocated or spent on these coastal protection solutions over the last few decades because of their perceived sustainability and ecologically positive co-benefits including habitat provision and carbon sequestration. These projects would benefit from integration of sediment transport pathways, budgets, and metrics during planning and modeling of restoration outcomes, in order to evaluate sustainability before investment. This is analogous to the decades of experience with coastal management and engineering on the open coast. Salt marshes are geomorphic features that rely partially on external sediment supply to maintain their network of tidal channels, intertidal flats, and marsh plain. Removing sediment from one component of the overall system to nourish another component may be counterproductive, given that the net sediment budget is unchanged. For example, dredging a tidal channel beyond its equilibrium condition will cause it to fill with sediment from the tidal flat or elsewhere in the system. This may cause slumping of the marsh edge, or over-deepening of other sections of the channel to compensate. Similarly, shoreline protection that prevents edge erosion hampers the marsh plain’s ability to accrete on the levee and naturally transgress landward or it starves other components of the system of regularly supplied sediment. A limited vertical or lateral-only perspective, instead of a three-dimensional perspective, during project planning and evaluation may lead to suboptimal decision-making regarding restoration priorities, approaches, and outcomes. I contend that before significant investments are made in marsh restoration through sediment nourishment or shoreline protection, sediment transport measurements and models that consider sediment dynamics should be integrated into the early phases of restoration planning. This will help identify where and under what conditions marsh restoration will most likely be successful and economically justified. Triaging and prioritizing is then possible, which is a sustainable approach for restoration, given the persistent vulnerability of marshes to sea-level rise, storms, and sediment deficits.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-019-00531-3","usgsCitation":"Ganju, N., 2019, Marshes are the new beaches: Integrating sediment transport into restoration planning: Estuaries and Coasts, v. 42, no. 4, p. 917-926, https://doi.org/10.1007/s12237-019-00531-3.","productDescription":"10 p.","startPage":"917","endPage":"926","ipdsId":"IP-103240","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467907,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-019-00531-3","text":"Publisher Index Page"},{"id":361288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"42","issue":"4","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":757309,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}