Recharge from preferential flow through mega-thick (100–1,000 m) unsaturated zones is a pervasive phenomenon, as demonstrated with a case study of volcanic highland recharge areas in the Great Basin province in southern Nevada, USA. Statistically significant rising water-level trends occur for most study-area wells and resulted from a relatively wet period (1969–2005) in south-central Nevada. Wet and dry winters control water-level trends, with water levels rising within a few months to a year following a wet-winter recharge event and declining during sustained dry periods. Even though a megadrought has persisted since 2000, this drought condition did not preclude major recharge events. Modern groundwater reaching the water table is consistent with previous geochemical studies of the study area that indicate mixing of modern and late Pleistocene recharge water. First-order approximations and simple mixing models of modern and late Pleistocene water indicate that 10 to 40 percent of recharge is preferential flow and that modern recharge may play a larger role in the water budget than previously thought.
The Republican River is the primary inflow to Milford Lake and drains areas of Kansas, Nebraska, and Colorado. Milford Lake has been listed as impaired and designated hypereutrophic by the Kansas Department of Health and Environment because of excessive nutrient loading. Milford Lake had confirmed harmful algal blooms every summer from 2011 through 2017 and in 2020 and 2021.
In the lower Republican River drainage basin, the Regional Conservation Partnership Program, administered by the Natural Resources Conservation Service, provides reimbursement to agricultural producers that implement best management practices intended to decrease sediment and nutrient runoff and loading into Milford Lake. Sediment and nutrient loads could potentially be driving factors in the development of harmful algal blooms in the reservoir.
Since July 2018, the U.S. Geological Survey, in cooperation with the Kansas Water Office, has collected continuous and discrete water-quality data at the Republican River at Clay Center, Kansas, streamgage (U.S. Geological Survey station 06856600), which is about 15 river miles upstream from Milford Lake. This report documents site-specific regression models for the computation of continuous concentrations of suspended sediment, total nitrogen, total phosphorus, and total carbon developed using continuous and discrete data collected from July 24, 2018, the date of continuous water-quality monitor installation, through March 31, 2021. The objective of this study is to characterize sediment and nutrient transport in the Milford Lake drainage basin before, during, and after best management practice implementation using the models described in this report.
The explanatory variable turbidity explained a high amount (72–96 percent) of the variance in suspended-sediment, total nitrogen, total phosphorus, and total carbon concentrations. Statistical plots for the four selected models showed the desired normality and homoscedasticity in residuals, and model standard error ratios indicated that recomputing each selected model after removing a randomly selected 10 percent of the data did not substantially change model coefficients.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225016","collaboration":"Prepared in cooperation with the Kansas Water Office","usgsCitation":"Leiker, B.M., 2022, Linear regression model documentation for computing water-quality constituent concentrations using continuous real-time water-quality data for the Republican River, Clay Center, Kansas, July 2018 through March 2021: U.S. Geological Survey Scientific Investigations Report 2022–5016, 13 p., https://doi.org/10.3133/sir20225016.","productDescription":"Report: vi, 13 p.; 4 Appendixes; Dataset","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-133566","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":396379,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"—USGS water data for the Nation"},{"id":396378,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5016/sir20225016.XML","linkFileType":{"id":8,"text":"xml"}},{"id":396376,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5016/sir20225016_appendix4.pdf","text":"Appendix 4","size":"757 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5016 Appendix 4","linkHelpText":"—Model Archive Summary for Total Carbon at U.S. Geological Survey Station 06856600, Republican River at Clay Center, Kansas, during July 2018 through March 2021"},{"id":396375,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5016/sir20225016_appendix3.pdf","text":"Appendix 3","size":"681 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5016 Appendix 3","linkHelpText":"—Model Archive Summary for Total Phosphorus at U.S. Geological Survey Station 06856600, Republican River at Clay Center, Kansas, during July 2018 through March 2021"},{"id":396374,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5016/sir20225016_appendix2.pdf","text":"Appendix 2","size":"788 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5016 Appendix 2","linkHelpText":"—Model Archive Summary for Total Nitrogen at U.S. Geological Survey Station 06856600, Republican River at Clay Center, Kansas, during July 2018 through March 2021"},{"id":396377,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5016/images"},{"id":396371,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5016/coverthb.jpg"},{"id":396372,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5016/sir20225016.pdf","text":"Report","size":"3.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5016"},{"id":396373,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5016/sir20225016_appendix1.pdf","text":"Appendix 1","size":"846 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5016 Appendix 1","linkHelpText":"—Model Archive Summary for Suspended Sediment at U.S. Geological Survey Station 06856600, Republican River at Clay Center, Kansas, during July 2018 through March 2021"}],"country":"United States","state":"Kansas","city":"Clay Center","otherGeospatial":"Republican River drainage basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.8170166015625,\n 39.03838632847035\n ],\n [\n -96.767578125,\n 39.172658670429946\n ],\n [\n -96.866455078125,\n 39.35129035526705\n ],\n [\n -97.01202392578125,\n 39.5146359327835\n ],\n [\n -97.0147705078125,\n 39.65857056750545\n ],\n [\n -96.99829101562499,\n 39.76632525654491\n ],\n [\n -97.14385986328125,\n 39.87601941962116\n ],\n [\n -97.4102783203125,\n 39.8992015115692\n ],\n [\n -97.74810791015625,\n 39.905522539728544\n ],\n [\n -97.92938232421875,\n 39.90762941952987\n ],\n [\n -98.22601318359375,\n 39.78954439311165\n ],\n [\n -98.228759765625,\n 39.65222681530652\n ],\n [\n -97.83050537109375,\n 39.50615988027491\n ],\n [\n -97.51190185546874,\n 39.42346418978382\n ],\n [\n -97.2344970703125,\n 39.3130504637139\n ],\n [\n -96.98181152343749,\n 39.034119526392836\n ],\n [\n -96.84997558593749,\n 39.00211029922515\n ],\n [\n -96.8170166015625,\n 39.03838632847035\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
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1217 Biltmore Drive
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Per- and polyfluoroalkyl substances (PFAS) are contaminants of concern due to their widespread occurrence in the environment, persistence, and potential to elicit a range of negative health effects. Per- and polyfluoroalkyl substances are regularly detected in surface waters, but their effects on many aquatic organisms are still poorly understood. Species with thyroid-dependent development, like amphibians, can be especially susceptible to PFAS effects on thyroid hormone regulation. We examined sublethal effects of aquatic exposure to four commonly detected PFAS on larval northern leopard frogs (Rana [Lithobates] pipiens), American toads (Anaxyrus americanus), and eastern tiger salamanders (Ambystoma tigrinum). Animals were exposed for 30 days (frogs and salamanders) or until metamorphosis (toads) to 10, 100, or 1000 μg/L of perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonate (PFHxS), or 6:2 fluorotelomer sulfonate (6:2 FTS). We determined that chronic exposure to common PFAS can negatively affect amphibian body condition and development at concentrations as low as 10 µg/L. These effects were highly species dependent, with species having prolonged larval development (frogs and salamanders) being more sensitive to PFAS than more rapidly developing species (toads). Our results demonstrate that some species could experience sublethal effects at sites with surface waters highly affected by PFAS. Our results also indicate that evaluating PFAS toxicity using a single species may not be sufficient for accurate amphibian risk assessment. Future studies are needed to determine whether these differences in susceptibility can be predicted from species' life histories and whether more commonly occurring environmental levels of PFAS could affect amphibians.
High fluoride (F) groundwaters (>1 mg/L) have been recognized as a water quality problem for nearly a century and occur in many countries worldwide. The affected aquifers can be sedimentary, metamorphic or igneous rocks, but the process giving rise to high-F concentrations has been studied with geochemical modeling and an examination of the rock sources. The association of high-F with silicic igneous rocks such as granites and rhyolites results from magmatic differentiation (fractional crystallization, fractional melting, and crustal assimilation) wherein F is enriched in the liquid phase because of its incompatibility in the mafic minerals that crystallize early during cooling. Further development of F-rich groundwaters occurs during the evolution of Na-HCO3 waters because of removal of Ca through ion-exchange and calcite precipitation, thereby raising the F concentration from minerals like fluorite and fluorapatite to maintain solubility equilibrium. Increasing temperatures enhance this effect because of the retrograde solubility of calcite. From geochemical modeling using the PhreeqcI code, the primary variables controlling F concentrations are DIC (dissolved inorganic carbon), salinity (ionic strength), PCO2, and temperature. Complexing is also important but plays a more secondary role. Considering these variables, an improved set of plotting parameters, F/Cl vs. HCO3/Cl, are shown to be effective in interpreting groundwater analyses. This approach is demonstrated by examining case studies from the Black Creek aquifer, South Carolina, USA, the Madison regional aquifer, midwestern USA, the Mizunami Underground Research Laboratory, Japan, New Zealand thermal waters, the San Luis Valley groundwaters, Colorado, USA, and the Aquia aquifer, Maryland, USA.
Gas hydrate production technologies commonly feature reservoir depressurization. Depressurization occurs when a pressure gradient is established in a well, drawing mobile water from the reservoir and reducing reservoir pressure. As such, the occurrence of mobile water is a necessary condition for effective gas production from gas hydrate reservoirs using common borehole-based methods. However, recent field programs have revealed that mobile water exists widely within the overall gas hydrate reservoir system, including within overlying and underlying units once thought of as virtually impermeable seals. Further, excess free water may also be commonly found in hydrate-free or hydrate-poor permeable strata interbedded within the larger gas hydrate reservoir system. Such internal sources of water are complex to characterize, difficult to explain, potentially highly heterogeneous, and may pose significant challenges to depressurization-based production. This report summarizes the general occurrence of water in gas hydrate systems and select technical implications.
Permeability of porous media, such as oil and gas reservoirs, is the crucial material parameter for predicting their hydraulic behavior. A nuclear magnetic resonance (NMR) analyzer is widely used as a powerful tool to predict permeability of various media. NMR T2 (transverse or spin–spin) relaxation time distribution, which is related to pore size distribution, gives the information to allow calculation of effective (initial) permeability. In this study, we investigate effective, intrinsic (absolute), and relative water and gas permeabilities of hydrate-bearing pressure core samples. These samples were recovered from the Alaska North Slope 2018 Hydrate-01 Stratigraphic Test Well by sidewall pressure coring and then analyzed in a laboratory using both fluid flow test and NMR analyzer. The peak of the NMR T2 distribution was measured at 10–20 ms using a laboratory NMR analyzer, which compares well with in situ measurements obtained via logging while drilling NMR data for two samples with high gas hydrate saturations (Sh = 76% and 74%). Further, comparison of laboratory NMR T2 distribution after hydrate dissociation revealed that the hydrate existed in large pore spaces. Effective permeabilities predicted by the Timur-Coates (TC) model and the Schlumberger-Doll-Research (SDR) model, with T2 cutoff 33 ms, were about an order of magnitude less than the laboratory measured values. Alternative TC model-based calculations with the T2 cutoff reduced to 10 ms and a newly developed hydraulic radius model better matched the laboratory data. For the analysis of the intrinsic permeabilities, the TC model with a T2 cutoff of 33 ms and SDR model were greater than the laboratory derived values, while the hydraulic radius model more closely matched the laboratory-derived values. In addition, permeability measurements were also made relative to gas and water under constant three-phase flow (water–gas–hydrate) conditions. After hydrate dissociation, a relative permeability curve was developed for each of the analyzed core samples based on the Corey petrophysical model. The results indicate that the gas permeability changed rapidly at high water saturation around 90%. Thus, we infer that the selection of relative reservoir parameters should focus on the higher water saturation conditions.
We analyzed impacts of interannual disturbance on the water balance of watersheds in different forested ecosystem case studies across the United States from 1985 to 2016 using a remotely sensed long-term land cover monitoring record (U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science products), gridded precipitation and evaporation data, and streamgaging data using paired watersheds (high and low disturbance). LCMAP products were used to quantify the timing and degree of interannual disturbance and to gain a better understanding of how land cover change affects the water balance of disturbed watersheds. In this paper, we present how LCMAP science products can be used to improve knowledge for hydrologic modeling, climate research, and forest management. Anthropogenic influences (e.g., dams and irrigation diversions) often minimize the impacts of land cover change on water balance dynamics when compared to interannual fluctuations of hydroclimatic events (e.g., drought and flooding). Our findings show that each watershed exhibits a complex suite of influences involving climate variables and other factors that affect each of their water balances differently when land cover change occurs. In this study, forests within arid to semi-arid climates experience greater water balance effects from land cover change than watersheds where water is less limited.
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-117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"11","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Healey, Nathan C. 0000-0002-8516-2636","orcid":"https://orcid.org/0000-0002-8516-2636","contributorId":280023,"corporation":false,"usgs":false,"family":"Healey","given":"Nathan","email":"","middleInitial":"C.","affiliations":[{"id":57411,"text":"KBR, Inc.","active":true,"usgs":false}],"preferred":false,"id":835894,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rover, Jennifer 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Portal, California, USA","docAbstract":"Wildfires frequently affect the steep hillslopes near El Portal, California (United States), a small community established during the California Gold Rush in the mid-1800s. In addition to the historical significance of El Portal, State Route 140 (SR 140) is a major transportation and economic corridor connecting the San Joaquin Valley to Yosemite National Park (YNP). In 2019, an estimated 4.5 million tourists visited and accessed YNP via SR 140. In the years after wildfires, the burned watersheds produced debris flows during intense rainfall, impacting the El Portal community and motorists traveling on SR 140 and local roads. The steepness of the hillslopes and confinement of the valley limit options for mitigating debris-flow risk. As such, emergency managers are left with evacuation orders or temporary road closures as the best options for risk reduction. The effectiveness of these options is highly dependent on establishing an accurate local rainfall intensity-duration threshold that officials can use to guide emergency response actions and timing. We present an overview of the rainfall conditions that initiated 12 post-fire debris-flow events near El Portal from 1991 to 2018 and objectively define rainfall intensity-duration thresholds from triggering rainfall rates. Our results highlight the modest rainfall rates that triggered debris flows in these steep watersheds, while radar data from more recent events (2012–2018) portray the spatial variability of intense rainfall in the area. Additional rainfall monitoring is needed to provide a robust rainfall threshold that will effectively mitigate risk for residents and motorists while minimizing the impact of road closures and evacuations.
","language":"English","publisher":"Association of Environmental & Engineering Geologists","doi":"10.2113/EEG-D-21-00031","usgsCitation":"De Graff, J.V., Staley, D.M., Stock, G., Takenaka, K., Gallegos, A., and Neptune, C., 2022, Rainfall triggering of post-fire debris flows over a 28-year period near El Portal, California, USA: Environmental and Engineering Geoscience, v. 28, no. 1, p. 133-145, https://doi.org/10.2113/EEG-D-21-00031.","productDescription":"14 p.","startPage":"133","endPage":"145","ipdsId":"IP-134684","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":483478,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"El Portal, Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.333,\n 38\n ],\n [\n -120.25,\n 38\n ],\n [\n -120.25,\n 37.333\n ],\n [\n -119.333,\n 37.333\n ],\n [\n -119.333,\n 38\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"28","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"De Graff, Jerome V.","contributorId":195393,"corporation":false,"usgs":false,"family":"De Graff","given":"Jerome","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":931121,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":931122,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stock, Greg M.","contributorId":258810,"corporation":false,"usgs":false,"family":"Stock","given":"Greg M.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":931123,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Takenaka, Kellen","contributorId":352407,"corporation":false,"usgs":false,"family":"Takenaka","given":"Kellen","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":931124,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gallegos, Alan L.","contributorId":352408,"corporation":false,"usgs":false,"family":"Gallegos","given":"Alan L.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":931125,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Neptune, Chad K.","contributorId":352411,"corporation":false,"usgs":false,"family":"Neptune","given":"Chad K.","affiliations":[{"id":84211,"text":"California State University, Fresno CA USA","active":true,"usgs":false}],"preferred":false,"id":931126,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229156,"text":"70229156 - 2022 - DSWEmod - The production of high-frequency surface water map composites from daily MODIS images","interactions":[],"lastModifiedDate":"2022-04-12T13:36:29.136585","indexId":"70229156","displayToPublicDate":"2022-02-21T06:51:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"DSWEmod - The production of high-frequency surface water map composites from daily MODIS images","docAbstract":"Optical satellite imagery is commonly used for monitoring surface water dynamics, but clouds and cloud shadows present challenges in assembling complete water time series. To test whether the daily revisit rate of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery can reduce cloud obstruction and improve high-frequency surface water mapping, we compared map results derived from Landsat (30-m) and MODIS (250-m) data across the state of California for 2003–2019. We adapted the Dynamic Surface Water Extent (DSWE) model in Google Earth Engine to generate surface water map composites from MODIS imagery every 5, 10, 15, and 30 days, and compared products to monthly Landsat-based DSWE maps. Results for DSWEmod (DSWE MODIS) in California suggest that more than 5% data loss (cloud obstruction, etc.) was present in only 2% of the 15-day time series, as compared to 32% of the monthly Landsat DSWE time series. The five-day DSWEmod composites averaged 8.4% obscuration in the winter months. Area estimates derived from cloud-filtered MODIS and Landsat monthly products have the highest linear correlations compared to streamgage discharge records, suggesting that monthly scale analyses best explain the relationship between surface water area and general streamflow dynamics. Shorter-interval DSWEmod products have lower correlations but utility for understanding the timing of surface water peaks and past flood events.
Lead (Pb) occurrence and sources and aqueous geochemistry were assessed in private wellhead and tap water at a targeted area of concern for possible exceedances and at a control area in the same geologic formation, and in wells at a nearby landfill in south-central Massachusetts (MA). Total Pb concentrations were below the U.S. Environmental Protection Agency (USEPA) Action Level of 15 μg/L in all samples, and about 6% of unfiltered samples contained Pb concentrations that exceeded 1.0 μg/L. Pb concentrations were higher under conditions that are acidic and oxic (pH ≤ 6.5 and dissolved oxygen [DO] ≥ 2 mg/L), in which minerals that could sequester lead or manganese typically are undersaturated, and adsorption by hydrous ferric oxide is limited. Under more neutral to alkaline conditions, the precipitation of Pb in solid solution series minerals such as (Ca,Pb)CO3 and (Ba,Pb)SO4−2, and adsorption by amorphous ferric hydroxides, could limit Pb solubility in the bedrock aquifer or in the plumbing. The low Pb concentrations and the absence of distinctive Pb and strontium (Sr) isotope ratio patterns in samples indicate that a nearby landfill is not likely a significant Pb source. Dissolved concentrations of Pb, copper (Cu), and zinc (Zn) in tap samples were significantly greater than those in wellhead samples, indicating that some Pb is derived from plumbing. Wellhead or tap samples with the highest Pb concentrations also had the greatest corrosivity potential based on the calcite saturation index and the PPGC (Potential to Promote Galvanic Corrosion) and supports the premise that Pb concentrations in tap samples were derived partly from corrosion of plumbing. Concentrations of other constituents, including arsenic (As), uranium (U), Sr, boron (B), and lithium (Li) were not statistically different between the tap and wellhead samples but, apart from Sr, all were statistically higher in the control area than in the target area. This variation in constituent concentrations suggests geochemical variation within the host Paxton Formation, possibly related to faulting and contact with the Ayer granite east of the control area.
This chapter provides an overview of the main drivers of change in estuarine systems, their expected causes and impacts on estuarine fish and fisheries. An analysis of global, regional and local patterns of estuarine fish and how climate-induced change may impact estuarine systems and their fish communities is provided. We also examine the main environmental, climatic and biological stressors likely to impact estuarine fish and associated fisheries. A set of case studies is used to illustrate the differences in potential impacts associated with various global regions and types of estuaries. An understanding of climate change in estuaries will support estuarine ecosystem resilience, inform management and facilitate adaptation.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Fish and fisheries in estuaries: A global perspective","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Wiley","doi":"10.1002/9781119705345.ch7","usgsCitation":"Gillanders, B.M., McMillan, M.N., Reis-Santos, P., Baumgartner, L.J., Brown, L.R., Conallin, J., Feyrer, F.V., Henriques, S., James, N.C., Jaureguizar, A.J., Pessanha, A.L., Vasconcelos, R.P., Vu, A., Walther, B., and Wibowo, A., 2022, Climate change and fishes in estuaries, chap. 7 of Fish and fisheries in estuaries: A global perspective, p. 380-457, https://doi.org/10.1002/9781119705345.ch7.","productDescription":"78 p.","startPage":"380","endPage":"457","ipdsId":"IP-116180","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":407966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Gillanders, Bronwyn M","contributorId":291280,"corporation":false,"usgs":false,"family":"Gillanders","given":"Bronwyn","email":"","middleInitial":"M","affiliations":[{"id":62654,"text":"School of Biological Sciences, and Environment Institute, University of Adelaide","active":true,"usgs":false}],"preferred":false,"id":853893,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McMillan, Matthew N.","contributorId":297357,"corporation":false,"usgs":false,"family":"McMillan","given":"Matthew","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":853894,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reis-Santos, P.","contributorId":93283,"corporation":false,"usgs":true,"family":"Reis-Santos","given":"P.","email":"","affiliations":[],"preferred":false,"id":853895,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baumgartner, Lee J.","contributorId":203990,"corporation":false,"usgs":false,"family":"Baumgartner","given":"Lee","email":"","middleInitial":"J.","affiliations":[{"id":36787,"text":"Charles Sturt University, Institute for Land, Water, and Society","active":true,"usgs":false}],"preferred":false,"id":853896,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788727,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Conallin, John","contributorId":220478,"corporation":false,"usgs":false,"family":"Conallin","given":"John","email":"","affiliations":[{"id":40173,"text":"Charles Sturt University","active":true,"usgs":false}],"preferred":false,"id":853897,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788726,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Henriques, Sofia","contributorId":297358,"corporation":false,"usgs":false,"family":"Henriques","given":"Sofia","email":"","affiliations":[],"preferred":false,"id":853898,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"James, Nicola C.","contributorId":297359,"corporation":false,"usgs":false,"family":"James","given":"Nicola","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":853899,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jaureguizar, Andres J","contributorId":297360,"corporation":false,"usgs":false,"family":"Jaureguizar","given":"Andres","email":"","middleInitial":"J","affiliations":[],"preferred":false,"id":853900,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pessanha, Andre L. M.","contributorId":297361,"corporation":false,"usgs":false,"family":"Pessanha","given":"Andre","email":"","middleInitial":"L. M.","affiliations":[],"preferred":false,"id":853901,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Vasconcelos, Rita P.","contributorId":297362,"corporation":false,"usgs":false,"family":"Vasconcelos","given":"Rita","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":853902,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Vu, An V.","contributorId":297363,"corporation":false,"usgs":false,"family":"Vu","given":"An V.","affiliations":[],"preferred":false,"id":853903,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Walther, Benjamin","contributorId":297364,"corporation":false,"usgs":false,"family":"Walther","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":853904,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wibowo, Arif","contributorId":220488,"corporation":false,"usgs":false,"family":"Wibowo","given":"Arif","email":"","affiliations":[{"id":40178,"text":"Ministry of Marine Affairs and Fisheries, Indonesia","active":true,"usgs":false}],"preferred":false,"id":853905,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70228753,"text":"fs20223005 - 2022 - South Carolina and Landsat","interactions":[],"lastModifiedDate":"2023-01-24T11:49:10.467656","indexId":"fs20223005","displayToPublicDate":"2022-02-18T09:50:49","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3005","displayTitle":"South Carolina and Landsat","title":"South Carolina and Landsat","docAbstract":"South Carolina, the eighth State admitted to the union, transcends its size with its deep, rich history; striking beauty; vast natural resources; and extensive cultural diversity. Home to part of the Blue Ridge Mountains of the Central Appalachians, the Upstate is graced with more than 100 waterfalls, while the Lowcountry borders the Atlantic Ocean with 187 miles of coastline and 35 barrier islands. Forests cover two-thirds of the State, and forestry and agriculture together, as agribusiness, make up South Carolina’s leading industry. Two historic crops—cotton and tobacco—still rank in the top 10 commodities, though corn and soybeans now rank higher. Poultry, cattle, peanuts, and flowers also make the list.
South Carolina’s population totals more than five million. Other residents include a variety of wildlife, bird, reptile, and fish species, including Ursus americanus (black bears), Alligator mississippiensis (American alligators), and Tursiops truncatus (bottlenose dolphins). More than 100 tree species also reside in South Carolina, which pays homage to one with its “The Palmetto State” nickname.
South Carolina’s subtropical climate, long coastline, and lower elevations make it highly susceptible to tornado and hurricane activity and coastal flooding. Projected sea-level rise is a growing concern. A view from space can help monitor and manage natural resources on the land and in rivers, marshes, and the coast. Landsat reveals not just what an area looks like now, but also insights from decades ago.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Reliable estimates of the magnitude of peak flows in streams are required for the economical and safe design of transportation and water conveyance structures. In addition, reliable estimates of the magnitude of low flows at defined frequencies and durations are needed for meeting regulatory requirements, quantifying base flows in streams and rivers, and evaluating time of travel and dilution of toxic spills. This report, in cooperation with the Delaware Department of Transportation and the Delaware Geological Survey, presents methods for estimating the magnitude of peak flows and low flows at defined frequencies and durations on nontidal streams in Delaware, at locations both monitored by streamflow-gage sites and ungaged. Methods are presented for estimating (1) the magnitude of peak flows for return periods ranging from 2 to 500 years (50-percent to 0.2-percent annual-exceedance probability), and (2) the magnitude of low flows as applied to 7-, 14-, and 30-consecutive day low-flow periods with recurrence intervals of 2, 10, and 20 years (50-, 10-, and 5-percent annual non-exceedance probabilities). These methods are applicable to watersheds that exhibit a full range of development conditions in Delaware. The report also describes StreamStats, a web application that allows users to easily obtain peak-flow and low-flow magnitude estimates for user-selected locations in Delaware.
Peak-flow and low-flow magnitude estimates for ungaged sites are obtained using statistical regression analysis through a process known as regionalization, where information from a group of streamflow-gage sites within a region forms the basis for estimates for ungaged sites within the same region. Ninety-four streamflow-gage sites in and near Delaware with at least 10 years of nonregulated annual peak-flow data were used for the peak-flow regression analysis, a subset of the 121 sites for which peak-flow estimates were computed. These sites included both continuous-record streamflow-gage sites as well as partial record sites. Forty-five streamflow-gage sites with at least 10 years of nonregulated low-flow data available were used for the low-flow regression analyses, a subset of the 68 sites for which low-flow estimates were computed. Estimates for gaged sites are obtained by combining (1) the station peak-flow statistics (mean, standard deviation, and skew) and peak-flow estimates using the recent Bulletin 17C guidelines that incorporate the Expected Moments Algorithm with (2) regional estimates of peak-flow magnitude derived from regional regression equations and regional skew derived from sites with records greater than or equal to 35 years. Example peak-flow estimate calculations using the methods presented in the report are given for (1) ungaged sites, (2) gaged sites, (3) sites upstream or downstream from a gaged location, and (4) sites between gaged locations. Estimates for low-flow gaged sites are obtained by combining (1) the station low-flow statistics (mean, standard deviation, and skew) and low-flow estimates with (2) regional estimates of low-flow magnitude derived from regional regression equations. Example low-flow estimate calculations using the methods presented in the report are given for (1) ungaged sites, (2) gaged sites, (3) sites upstream or downstream from a gaged location, and (4) sites between gaged locations. A total of 54 sites in the Coastal Plain region were used to develop peak-flow regressions for the region and 40 sites were used for the Piedmont region. Similarly, 24 sites were used for low-flow regression equation development in the Coastal Plain, with 21 in the Piedmont. Peak and low-flow site inclusion in the Coastal Plain tended to be more restricted with tidal influence and ranges of basin characteristics, including drainage area, limiting regression equation development and application.
Regional regression equations for peak flows and low flows, as applicable to ungaged sites in the Piedmont and Coastal Plain Physiographic Provinces in Delaware, are presented. Peak-flow regression equations used variables that quantified drainage area, basin slope, percent area with well-drained soils, percent area with poorly drained soils, impervious area, and percent area of surface water storage in estimating peak-flow estimates, whereas low-flow regression equations used only drainage area and percent poorly drained soils in the estimation of low flows. Average standard errors for peak-flow regressions tended to be lower than those for low- flow regressions, with lower errors in the Piedmont region for both peak- and low-flow regressions. For peak-flow estimates, a sensitivity analysis of Piedmont regression equation estimates to changes in impervious area is also presented.
Additional topics associated with the analyses performed during the study are discussed, including (1) the availability and description of 32 basin and climatic characteristics considered during the development of the regional regression equations; (2) the treatment of increasing trends in the annual peak-flow series identified at 18 gaged sites and inclusion in or exclusion from the regional analysis; (3) regional skew analysis and determination of regression regions; (4) sample adjustments and removal of sites owing to regulation and redundancy; and (5) a brief comparison of peak- and low-flow estimates at gages used in previous studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225005","collaboration":"Prepared in cooperation with the Delaware Department of Transportation and the Delaware Geological Survey","usgsCitation":"Hammond, J.C., Doheny, E.J., Dillow, J.J.A., Nardi, M.R., Steeves, P.A., and Warner, D.L., 2022, Peak-flow and low-flow magnitude estimates at defined frequencies and durations for nontidal streams in Delaware: U.S. Geological Survey Scientific Investigations Report 2022–5005, 46 p., https://doi.org/10.3133/sir20225005.","productDescription":"Report: vi, 46 p.; 4 Data Releases","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127314","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":502293,"rank":10,"type":{"id":36,"text":"NGMDB Index 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Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Municipal wastewater (MWW) and mine drainage (MD) are common co-occurring sources of freshwater pollution in mining regions. The physicochemical interactions that occur after mixing MWW and MD in a waterway may improve downstream water quality of an impaired reach by reducing downstream concentrations of nutrients and metals (i.e., “co-attenuation”). A first-order stream (Bradley Run in central Pennsylvania), with coal MD and secondarily treated MWW entering the stream in the same location, was systematically monitored to determine in-stream water-quality dynamics. Monitored constituents included pH, nutrients (i.e., phosphorus and nitrogen), and metals (e.g., iron, aluminum, manganese). Mixing of the MWW, MD, and upstream water decreased concentrations of phosphate, aluminum, and iron by 94%, 91%, and 98%, respectively, relative to conservative mixtures at the 1400-m-downstream site. The pollutant co-attenuation resulted in water quality equivalent to that upstream of the pollutant sources and improved the phosphorus-based trophic status of the stream. Geochemical models indicate the primary mechanisms for P attenuation in the studied stream were precipitation as variscite (AlPO4:2H2O) or amorphous AlPO4 plus adsorption to hydrous ferric oxide, despite a much greater abundance of hydrous aluminum oxide. The results presented in this study suggest that in-stream mixing of MD with untreated or secondarily treated MWW may be an important, overlooked factor affecting downstream transport of common pollutants in mining regions. Decreased metals loading and increased pH resulting from natural attenuation and remediation of MD could affect the potential for retention of phosphate by stream sediment and could lead to the release of nutrients from legacy accumulations, highlighting the potential need to address high-nutrient discharges (e.g., improved MWW treatment) in concert with MD remediation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2022.118173","usgsCitation":"Spellman, C.J., Smyntek, P.M., Cravotta, C., Tasker, T.L., and Strosnider, W.H., 2022, Pollutant co-attenuation via in-stream interactions between mine drainage and municipal wastewater: Water Research, v. 214, 118173, 10 p., https://doi.org/10.1016/j.watres.2022.118173.","productDescription":"118173, 10 p.","ipdsId":"IP-134190","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":400756,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"214","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Spellman, Charles J.","contributorId":291844,"corporation":false,"usgs":false,"family":"Spellman","given":"Charles","email":"","middleInitial":"J.","affiliations":[{"id":62771,"text":"Department of Civil and Environmental Engineering, University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":843213,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smyntek, Peter M.","contributorId":291642,"corporation":false,"usgs":false,"family":"Smyntek","given":"Peter","email":"","middleInitial":"M.","affiliations":[{"id":62738,"text":"Saint Vincent College","active":true,"usgs":false}],"preferred":false,"id":843214,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843215,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tasker, Travis L.","contributorId":211456,"corporation":false,"usgs":false,"family":"Tasker","given":"Travis","email":"","middleInitial":"L.","affiliations":[{"id":38248,"text":"Civil and Environmental Engineering Department, The Pennsylvania State University,","active":true,"usgs":false}],"preferred":false,"id":843216,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Strosnider, William H. J.","contributorId":291845,"corporation":false,"usgs":false,"family":"Strosnider","given":"William","email":"","middleInitial":"H. J.","affiliations":[{"id":62772,"text":"Baruch Institute for Marine and Coastal Sciences, University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":843217,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228754,"text":"70228754 - 2022 - Subsurface hydrocarbon degradation strategies in low- and high-sulfate coal seam communities identified with activity-based metagenomics","interactions":[],"lastModifiedDate":"2022-02-18T14:08:59.596133","indexId":"70228754","displayToPublicDate":"2022-02-17T08:03:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10120,"text":"npj Biofilms and Microbiomes","active":true,"publicationSubtype":{"id":10}},"title":"Subsurface hydrocarbon degradation strategies in low- and high-sulfate coal seam communities identified with activity-based metagenomics","docAbstract":"Environmentally relevant metagenomes and BONCAT-FACS derived translationally active metagenomes from Powder River Basin coal seams were investigated to elucidate potential genes and functional groups involved in hydrocarbon degradation to methane in coal seams with high- and low-sulfate levels. An advanced subsurface environmental sampler allowed the establishment of coal-associated microbial communities under in situ conditions for metagenomic analyses from environmental and translationally active populations. Metagenomic sequencing demonstrated that biosurfactants, aerobic dioxygenases, and anaerobic phenol degradation pathways were present in active populations across the sampled coal seams. In particular, results suggested the importance of anaerobic degradation pathways under high-sulfate conditions with an emphasis on fumarate addition. Under low-sulfate conditions, a mixture of both aerobic and anaerobic pathways was observed but with a predominance of aerobic dioxygenases. The putative low-molecular-weight biosurfactant, lichysein, appeared to play a more important role compared to rhamnolipids. The methods used in this study—subsurface environmental samplers in combination with metagenomic sequencing of both total and translationally active metagenomes—offer a deeper and environmentally relevant perspective on community genetic potential from coal seams poised at different redox conditions broadening the understanding of degradation strategies for subsurface carbon.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41522-022-00267-2","usgsCitation":"Schweitzer, H.S., Smith, H.J., Barnhart, E.P., McKay, L.J., Gerlach, R., Cunningham, A.B., Malmstrom, R.R., Goudeau, D., and Fields, M.W., 2022, Subsurface hydrocarbon degradation strategies in low- and high-sulfate coal seam communities identified with activity-based metagenomics: npj Biofilms and Microbiomes, v. 8, 7, 10 p., https://doi.org/10.1038/s41522-022-00267-2.","productDescription":"7, 10 p.","ipdsId":"IP-126104","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":448742,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41522-022-00267-2","text":"Publisher Index Page"},{"id":396170,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2022-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Schweitzer, Hannah S.","contributorId":268345,"corporation":false,"usgs":false,"family":"Schweitzer","given":"Hannah","email":"","middleInitial":"S.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":835318,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Heidi J.","contributorId":268344,"corporation":false,"usgs":false,"family":"Smith","given":"Heidi","email":"","middleInitial":"J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":835319,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835320,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKay, Luke J.","contributorId":268349,"corporation":false,"usgs":false,"family":"McKay","given":"Luke","email":"","middleInitial":"J.","affiliations":[{"id":55631,"text":"Center for Biofilm Engineering, Montana State University, Bozeman","active":true,"usgs":false}],"preferred":false,"id":835321,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gerlach, Robin","contributorId":203247,"corporation":false,"usgs":false,"family":"Gerlach","given":"Robin","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":835322,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cunningham, Alfred B.","contributorId":172389,"corporation":false,"usgs":false,"family":"Cunningham","given":"Alfred","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":835323,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Malmstrom, Rex R.","contributorId":268350,"corporation":false,"usgs":false,"family":"Malmstrom","given":"Rex","email":"","middleInitial":"R.","affiliations":[{"id":55632,"text":"DOE Joint Genome Institute","active":true,"usgs":false}],"preferred":false,"id":835324,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goudeau, Danielle","contributorId":268351,"corporation":false,"usgs":false,"family":"Goudeau","given":"Danielle","email":"","affiliations":[{"id":55632,"text":"DOE Joint Genome Institute","active":true,"usgs":false}],"preferred":false,"id":835405,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fields, Matthew W.","contributorId":172391,"corporation":false,"usgs":false,"family":"Fields","given":"Matthew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":835325,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70237304,"text":"70237304 - 2022 - Effects of weather variation on waterfowl migration: Lessons from a continental-scale generalizable avian movement and energetics model","interactions":[],"lastModifiedDate":"2022-10-07T12:24:33.871983","indexId":"70237304","displayToPublicDate":"2022-02-17T07:19:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Effects of weather variation on waterfowl migration: Lessons from a continental-scale generalizable avian movement and energetics model","docAbstract":"We developed a continental energetics-based model of daily mallard (Anas platyrhynchos) movement during the non-breeding period (September to May) to predict year-specific migration and overwinter occurrence. The model approximates movements and stopovers as functions of metabolism and weather, in terms of temperature and frozen precipitation (i.e., snow). The model is a Markov process operating at the population level and is parameterized through a review of literature. We applied the model to 62 years of daily weather data for the non-breeding period. The average proportion of available habitat decreased as weather severity increased, with mortality decreasing as the proportion of available habitat increased. The most commonly used locations during the course of the non-breeding period were generally consistent across years, with the most inter-annual variation present in the overwintering area. Our model revealed that the distribution of mallards on the landscape changed more dramatically when the variation in daily available habitat was greater. The main routes for avian migration in North America were predicted by our simulations: the Atlantic, Mississippi, Central, and Pacific flyways. Our model predicted an average of 77.4% survivorship for the non-breeding period across all years (range = 76.4%–78.4%), with lowest survivorship during autumn (90.5 ± 1.4%), intermediate survivorship in winter (91.8 ± 0.7%), and greatest survivorship in spring (93.6 ± 1.1%). We provide the parameters necessary for exploration within and among other taxa to leverage the generalizability of this migration model to a broader expanse of bird species, and across a range of climate change and land use/land cover change scenarios.
Most wildfires are started by humans, however, geographic variation of potential ignition sources is not often explicitly accounted for in wildfire simulation modelling or risk assessments. In this study, we investigated how patterns of human and lightning ignitions can influence modelled fire simulations and demonstrate how these data can be used to assess post-fire flooding and sediment transport. We used historical ignition data (1992–2015) to characterize ignition patterns for thirteen mountain ranges in southern Arizona, United States, and developed FlamMap burn probability (BP) models for three scenarios: human ignition, lightning ignition, and random ignition. We then developed a watershed-scale case study assessing the impacts of ignition scenarios on post-fire hydrology using the KINEROS2 model that simulates runoff and erosion. BP models illustrated considerable differences in landscape fire risk between the three ignition scenarios. Results from the watershed model indicate the greatest impacts from the post-fire human ignition scenario, with a 10-fold increase in sediment discharge and four-fold increase in peak flow compared to pre-fire conditions. Our results show that consideration of ignition source and location is important for assessing fire risk, and our modelling approach provides a planning mechanism to identify locations most at risk to fire-induced flood hazards, where prevention and mitigation activities can be focused.
Although rivers are of immense practical, aesthetic, and recreational value, these aquatic habitats are particularly sensitive to environmental changes. Increasingly, changes in streamflow and water quality are resulting in blooms of bottom-attached (benthic) algae, also known as periphyton, which have become widespread in many water bodies of US national parks. Because these blooms degrade visitor experiences and threaten human and ecosystem health, improved methods of characterizing benthic algae are needed. This study evaluated the potential utility of remote sensing techniques for mapping variations in algal density in shallow, clear-flowing rivers. As part of an initial proof-of-concept investigation, field measurements of water depth and percent cover of benthic algae were collected from two reaches of the Buffalo National River along with aerial photographs and multispectral satellite images. Applying a band ratio algorithm to these data yielded reliable depth estimates, although a shallow bias and moderate level of precision were observed. Spectral distinctions among algal percent cover values ranging from 0 to 100% were subtle and became only slightly more pronounced when the data were aggregated to four ordinal levels. A bagged trees machine learning model trained using the original spectral bands and image-derived depth estimates as predictor variables was used to produce classified maps of algal density. The spatial and temporal patterns depicted in these maps were reasonable but overall classification accuracies were modest, up to 64.6%, due to a lack of spectral detail. To further advance remote sensing of benthic algae and other periphyton, future studies could adopt hyperspectral approaches and more quantitative, continuous metrics such as biomass.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14040953","usgsCitation":"Legleiter, C.J., and Hodges, S.W., 2022, Mapping benthic algae and cyanobacteria in river channels from aerial photographs and satellite images: A proof-of-concept investigation on the Buffalo National River, AR, USA: Remote Sensing, v. 14, no. 4, 953, 28 p., https://doi.org/10.3390/rs14040953.","productDescription":"953, 28 p.","ipdsId":"IP-136035","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":448762,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14040953","text":"Publisher Index Page"},{"id":435963,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J5QXDJ","text":"USGS data release","linkHelpText":"Remotely sensed data and field measurements of water depth and percent cover of benthic algae from two reaches of the Buffalo National River in Arkansas acquired in August 2021"},{"id":396175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Buffalo National River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.438720703125,\n 35.917971791312816\n ],\n [\n -92.00225830078125,\n 35.917971791312816\n ],\n [\n -92.00225830078125,\n 36.22876574685929\n ],\n [\n -93.438720703125,\n 36.22876574685929\n ],\n [\n -93.438720703125,\n 35.917971791312816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-02-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":835333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hodges, Shawn W 0000-0002-8950-7232","orcid":"https://orcid.org/0000-0002-8950-7232","contributorId":279667,"corporation":false,"usgs":false,"family":"Hodges","given":"Shawn","email":"","middleInitial":"W","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":835334,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229665,"text":"70229665 - 2022 - Managing multiple species with conflicting needs in the Greater Everglades","interactions":[],"lastModifiedDate":"2023-06-09T13:50:36.683544","indexId":"70229665","displayToPublicDate":"2022-02-16T08:10:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Managing multiple species with conflicting needs in the Greater Everglades","docAbstract":"Given limited funding, natural resources decision making is riddled with tradeoffs, including which species or landscapes to prioritize for management action. Florida’s Everglades wetland is home to numerous indicator species, some of which are endangered. But with a multitude of species comes differing hydrologic requirements to yield appropriate foraging and breeding conditions for each. The Everglades ecosystem is highly managed, with water being moved across the landscape to meet the habitat and reproductive needs of species of concern. Predictive modeling can help water managers understand potential consequences to targeted water conditions. EverForecast is a novel spatially explicit, hydrologic, and ecological operational forecast developed to inform conservation management decisions. Not only does EverForecast provide probable near-term water conditions, but also predicted species responses to those hydrologic conditions. Using examples from two focal regions of the Everglades, we show the magnitude of impacts to a suite of species and an almost 70% decline in suitable conditions for one species when prioritizing water management to meet the needs of another species. Although EverForecast is a relatively new decision support tool, its hydrologic outputs are already commonly used to make water management recommendations because it provides near-term hydrologic forecasts that scientists and managers need for water operations decision making. Because species management decisions have historically been made to target a single species at a time, it may take longer for full utility of EverForecast’s ability to quantify tradeoffs among species to become integrated into decision making.
The U.S. Geological Survey (USGS) Rocky Mountain Region (RMR) hosted USGS scientists, managers, program coordinators, and leadership team members for a virtual Science Exchange during September 15–17, 2020. The Science Exchange had 216 registered participants and included 48 talks over the 3-day period. Invited speakers presented information about the novel USGS Earth Monitoring, Analysis, and Prediction (EarthMAP) concept. Scientists in the RMR and other regions showcased their research and participated in discussions related to the EarthMAP concept and EarthMAP applications. In addition, the Colorado River Basin Pilot Project, the first formal EarthMAP pilot project, was unveiled during the Science Exchange. Many of the products designed during the RMR Science Exchange were done so with the EarthMAP – Colorado River Basin Pilot Project in mind. This report summarizes the organization and objectives of the Science Exchange, highlights key points from session presentations, panel discussions, and breakout sessions, and, most importantly, discusses momentum generated for the EarthMAP – Colorado River Basin Pilot Project.
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U.S. Geological Survey
P.O. Box 25046, MS 911
Denver, CO 80225–0046
The Atchafalaya River Basin is the largest remaining forested wetland in the contiguous United States. Since 1960, dredging and channel erosion in the Basin have resulted in changes to the hydrologic connectivity that have not been quantified. Analyses were conducted to determine the hydraulic and geomorphic factors that have changed since discharge became controlled that may have decreased river/floodplain connectivity. We examined: (1) stage/discharge relationships from 1960 to 2014; (2) hydroperiods across the floodplain; (3) discharge distribution to the floodplain by comparing discharge measurements from 1959–1968 to 2005–2012; and (4) channel cross-sections and floodplain elevations. Our results indicate that much of the floodplain no longer receives headwater discharge (upstream to downstream, > 200 km2) or receives too little discharge to alleviate stagnancy and hypoxia in the forested wetland at lower stages. Large portions of the Basin (400 km2) have low water levels controlled by channel geomorphology and sea-level rise that inundate the forested floodplain for more than 50% of the calendar year. This extended duration of inundation contributes to hypoxia and likely reduces nutrient retention. The confinement of discharge to a large efficient channel compromises the ability of this system to respond to sea-level rise and subsidence. This study provides insight to the effects of flood management projects along Coastal Plain rivers and deltas.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5347","usgsCitation":"Kroes, D., Demas, C.R., Allen, Y., Day, R., Roberts, S.W., and Varisco, J., 2022, Hydrologic modification and channel evolution degrades connectivity on the Atchafalaya River floodplain: Earth Surface Processes and Landforms, v. 47, no. 7, p. 1790-1807, https://doi.org/10.1002/esp.5347.","productDescription":"18 p.","startPage":"1790","endPage":"1807","ipdsId":"IP-094587","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":448787,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5347","text":"Publisher Index Page"},{"id":435966,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94GULXE","text":"USGS data release","linkHelpText":"Mean bed elevations of waterbodies on the Atchafalaya River floodplain"},{"id":398209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Atchafalaya River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.09814453125,\n 29.36302703778376\n ],\n [\n -90.977783203125,\n 29.36302703778376\n ],\n [\n -90.977783203125,\n 31.956823015897207\n ],\n [\n -93.09814453125,\n 31.956823015897207\n ],\n [\n -93.09814453125,\n 29.36302703778376\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Kroes, Daniel 0000-0001-9104-9077 dkroes@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-9077","contributorId":3830,"corporation":false,"usgs":true,"family":"Kroes","given":"Daniel","email":"dkroes@usgs.gov","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Demas, Charles R","contributorId":289813,"corporation":false,"usgs":false,"family":"Demas","given":"Charles","email":"","middleInitial":"R","affiliations":[{"id":38437,"text":"Retired, U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":839851,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Yvonne A.","contributorId":289815,"corporation":false,"usgs":false,"family":"Allen","given":"Yvonne A.","affiliations":[{"id":37461,"text":"fws","active":true,"usgs":false}],"preferred":false,"id":839852,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Day, Richard 0000-0002-5959-7054","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":222817,"corporation":false,"usgs":true,"family":"Day","given":"Richard","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839853,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roberts, Steve W","contributorId":289819,"corporation":false,"usgs":false,"family":"Roberts","given":"Steve","email":"","middleInitial":"W","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":839854,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Varisco, Jeff","contributorId":289821,"corporation":false,"usgs":false,"family":"Varisco","given":"Jeff","email":"","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":839855,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}