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  • 1
    Online Resource
    Online Resource
    New York, NY :Springer,
    Keywords: Environmental pollution. ; Electronic books.
    Description / Table of Contents: The book is based upon a lengthy review conducted by the Hypoxia Advisory Panel (HAP) of the Science Advisory Board for the Environmental Protection Agency (EPA) chaired by Virginia Dale. The report upon which the book is based has been extensively reviewed. The project's web site contains numerous review comments about the report, all of which have been addressed. In addition the draft report was review by four vetters who were paid by the EPA Science Advisory Board (SAB) to review the report. Those comments were addressed to the satisfaction of the EPA SAB Chapter Board. The book has been enthusiastically received by the SSEM series editors.
    Type of Medium: Online Resource
    Pages: 1 online resource (333 pages)
    Edition: 1st ed.
    ISBN: 9780387896861
    Series Statement: Springer Series on Environmental Management Series
    DDC: 363.73940916364
    Language: English
    Note: Intro -- Acknowledgments -- Contents -- List of Figures -- List of Tables -- Contributors -- Glossary -- List of Acronyms and Symbols -- Conversion Factors and Abbreviations -- Executive Summary -- Findings -- Recommendations for Monitoring and Research -- Recommendations for Adaptive Management -- Management Options -- Protecting and Enhancing Social Welfare in the Basin -- Conclusion -- 1 Introduction -- 1.1 Hypoxia and the Northern Gulf of Mexico A Brief Overview -- 1.2 Science and Management Goals for Reducing Hypoxia -- 1.3 Hypoxia Study Group -- 1.4 The Study Groups Approach -- 2 Characterization of Hypoxia -- 2.1 Historical Patterns and Evidence for Hypoxia on the Shelf -- 2.2 The Physical Context -- 2.2.1 Oxygen Budget: General Considerations -- 2.2.2 Vertical Mixing as a Function of Stratification and Vertical Shear -- 2.2.3 Changes in Mississippi River Hydrology and Their Effects on Vertical Mixing -- 2.2.4 Zones of Hypoxia Controls -- 2.2.5 Shelf Circulation: Local Versus Regional -- 2.3 Role of N and P in Controlling Primary Production -- 2.3.1 Nitrogen and Phosphorus Fluxes to the NGOM Background -- 2.3.2 N and P Limitation in Different Shelf Zones and Linkages Between High Primary Production Inshore and the Hypoxic Regions Farther Offshore -- 2.4 Other Limiting Factors and the Role of Si -- 2.5 Sources of Organic Matter to the Hypoxic Zone -- 2.5.1 Sources of Organic Matter to NGOM: Post 2000 Integrated Assessment -- 2.5.2 Advances in Organic Matter Understanding: Characterization and Processes -- 2.5.3 Synthesis Efforts Regarding Organic Matter Sources -- 2.6 Denitrification, P Burial, and Nutrient Recycling -- 2.7 Possible Regime Shift in the Gulf of Mexico -- 2.8 Single Versus Dual Nutrient Removal Strategies -- 2.9 Current State of Forecasting -- 3 Nutrient Fate, Transport, and Sources. , 3.1 Temporal Characteristics of Streamflow and Nutrient Flux -- 3.1.1 MARB Annual and Seasonal Fluxes -- 3.1.1.1 Annual Patterns -- 3.1.1.2 Seasonal Patterns -- 3.1.2 Subbasin Annual and Seasonal Flux -- 3.1.2.1 Annual Patterns -- 3.1.2.2 Annual Flux Estimates -- 3.1.2.3 Annual Yield Estimates -- 3.1.2.4 Seasonal Patterns -- 3.2 Mass Balance of Nutrients -- 3.2.1 Cropping Patterns -- 3.2.2 Nonpoint Sources -- 3.2.3 Point Sources -- 3.3 Nutrient Transport Processes -- 3.3.1 Aquatic Processes -- 3.3.2 Freshwater Wetlands -- 3.3.3 Nutrient Sources and Sinks in Coastal Wetlands -- 3.4 Ability to Route and Predict Nutrient Delivery to the Gulf -- 3.4.1 SPARROW Model -- 3.4.2 SWAT Model -- 3.4.3 IBIS/THMB Model -- 3.4.4 Discussion and Comparison of Models -- 3.4.5 Targeting -- 3.4.6 Model Uncertainty -- 4 Scientific Basis for Goals and Management Options -- 4.1 Adaptive Management -- 4.2 Setting Targets for Nitrogen and Phosphorus Reduction -- 4.3 Protecting Water Quality and Social Welfare in the Basin -- 4.3.1 Assessment and Review of the Cost Estimates from the CENR Integrated Assessment -- 4.3.2 Other Large-Scale Integrated Economic and Biophysical Models for Agricultural Nonpoint Sources -- 4.3.3 Research Assessing the Basin-Wide Co-benefits -- 4.3.4 Principles of Landscape Design -- 4.4 Cost-Effective Approaches for Nonpoint Source Control -- 4.4.1 Voluntary Programs -- Without Economic Incentives -- 4.4.2 Existing Agricultural Conservation Programs -- 4.4.3 Emissions and Water Quality Trading Programs -- 4.4.4 Agricultural Subsidies and Conservation Compliance Provisions -- 4.4.5 Taxes -- 4.4.6 Eco-labeling and Consumer Driven Demand -- 4.5 Options for Managing Nutrients, Co-benefits, and Consequences -- 4.5.1 Agricultural Drainage -- 4.5.1.1 Alternative Drainage System Design and Management -- 4.5.1.2 Bioreactors -- 4.5.2 Freshwater Wetlands. , 4.5.2.1 Nitrogen -- 4.5.2.2 Phosphorus -- 4.5.3 Conservation Buffers -- 4.5.4 Cropping Systems -- 4.5.5 Animal Production Systems -- 4.5.5.1 System Development and Nutrient Flows -- 4.5.5.2 Manure as a Component of N and P Mass Balances -- 4.5.5.3 Remedial Strategies -- 4.5.5.4 Alternative Manure Management Technologies -- 4.5.6 In-Field Nutrient Management -- 4.5.6.1 Fertilizer Sources -- 4.5.6.2 Fertilizer Use and Application Technology -- 4.5.6.3 Watershed-Scale Fertilizer Management -- 4.5.6.4 Controlled-Release Fertilizers -- 4.5.6.5 Effects of N Management on Soil Resource Sustainability -- 4.5.6.6 Precision Agriculture Management Tools for Nitrogen -- 4.5.6.7 Precision Agriculture Management Tools for Phosphorus -- 4.5.6.8 Nutrient Management Planning Strategies -- 4.5.7 Effective Actions for Other Nonpoint Sources -- 4.5.7.1 Atmospheric Deposition -- 4.5.7.2 Residential and Urban Sources -- 4.5.8 Most Effective Actions for Industrial and Municipal Sources -- 4.5.9 Ethanol and Water Quality in the MARB -- 4.5.9.1 Water Quality Implications of Projected Grain-Based Ethanol Production Levels -- 4.5.9.2 Impacts on Nutrient Application to Corn -- 4.5.9.3 Grain Versus Cellulosic Ethanol and Water Quality -- 4.5.10 Integrating Conservation Options -- 5 Summary of Findings and Recommendations -- 5.1 Characterization of Hypoxia -- 5.2 Nutrient Fate, Transport, and Sources -- 5.3 Goals and Management Options -- 5.4 Conclusion -- Appendices -- Appendix A: Studies on the Effects of Hypoxia on Living Resources -- Appendix B: Flow Diagrams and Mass Balance of Nutrients -- Global Material Cycles -- Atmospheric Deposition -- Appendix C: Animal Production Systems -- Intensification of Animal Feeding Operations -- Nutrient Budgets -- Nutrient Surpluses -- Targeting Remedial Strategies Within the MARB -- Managing Manures. , Crop Selected to Receive Manure Application -- Rate and Frequency of Application -- Intensity and Duration of Grazing -- Stream-Bank Fencing -- Appendix D: Calculation of Point Source Inputs of N and P -- Appendix E: USUSEPAs Guidance on Nutrient Criteria -- Comparison of SAB Nitrogen and Phosphorus Recommendations with USEPA Nitrogen and Phosphorus Criteria Recommended Reference Conditions ' Submitted by USEPA's Office of Water, 8-24-07. -- A More Comprehensive Approach -- References -- Subject Index.
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  • 2
    Publication Date: 2021-04-14
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 3
    Publication Date: 2017-10-18
    Description: State-of-the-art Arctic Ocean mean sea surface (MSS) models and global geoid models (GGMs) are used to support sea ice freeboard estimation from satellite altimeters, as well as in oceanographic studies such as mapping sea level anomalies and mean dynamic ocean topography. However, errors in a given model in the high frequency domain, primarily due to unresolved gravity features, can result in errors in the estimated along-track freeboard. These errors are exacerbated in areas with a sparse lead distribution in consolidated ice pack conditions. Additionally model errors can impact ocean geostrophic currents, derived from satellite altimeter data, while remaining biases in these models may impact longer-term, multi-sensor oceanographic time-series of sea level change in the Arctic. This study focuses on an assessment of five state-of-the-art Arctic MSS models (UCL13/04, DTU15/13/10) and a commonly used GGM (EGM2008). We describe errors due to unresolved gravity features, inter-satellite biases, and remaining satellite orbit errors, and their impact on the derivation of sea ice freeboard. The latest MSS models, incorporating CryoSat-2 sea surface height measurements, show improved definition of gravity features, such as the Gakkel Ridge. The standard deviation between models ranges 0.03-0.25 m. The impact of remaining MSS/GGM errors on freeboard retrieval can reach several decimeters in parts of the Arctic. While the maximum observed freeboard difference found in the central Arctic was 0.59 m (UCL13 MSS minus EGM2008 GGM), the standard deviation in freeboard differences is 0.03-0.06 m.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 4
    Publication Date: 2019-10-17
    Description: For ice concentrations less than 85%, internal ice stresses in the sea ice pack are small andsea ice is said to be in free drift. The sea ice drift is then the result of a balance between Coriolisacceleration and stresses from the ocean and atmosphere. We investigate sea ice drift using data fromindividual drifting buoys as well as Arctic-wide gridded fields of wind, sea ice, and ocean velocity. Weperform probabilistic inverse modeling of the momentum balance of free-drifting sea ice, implemented toretrieve the Nansen number, scaled Rossby number, and stress turning angles. Since this problem involvesa nonlinear, underconstrained system, we used a Monte Carlo guided search scheme—the NeighborhoodAlgorithm—to seek optimal parameter values for multiple observation points. We retrieve optimal dragcoefficients ofCA=1.2×10−3andCO=2.4×10−3from 10-day averaged Arctic-wide data from July 2014that agree with the AIDJEX standard, with clear temporal and spatial variations. Inverting daily averagedbuoy data give parameters that, while more accurately resolved, suggest that the forward model oversimplifies the physical system at these spatial and temporal scales. Our results show the importance of the correct representation of geostrophic currents. Both atmospheric and oceanic drag coefficients are found to decrease with shorter temporal averaging period, informing the selection of drag coefficient for short timescale climate models.
    Description: Published
    Description: 6388–6413
    Description: 5A. Ricerche polari e paleoclima
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 5
    Publication Date: 2022-10-26
    Description: Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research-Oceans 124(8), (2019): 6388-6413, doi: 10.1029/2018JC014881.
    Description: For ice concentrations less than 85%, internal ice stresses in the sea ice pack are small and sea ice is said to be in free drift. The sea ice drift is then the result of a balance between Coriolis acceleration and stresses from the ocean and atmosphere. We investigate sea ice drift using data from individual drifting buoys as well as Arctic‐wide gridded fields of wind, sea ice, and ocean velocity. We perform probabilistic inverse modeling of the momentum balance of free‐drifting sea ice, implemented to retrieve the Nansen number, scaled Rossby number, and stress turning angles. Since this problem involves a nonlinear, underconstrained system, we used a Monte Carlo guided search scheme—the Neighborhood Algorithm—to seek optimal parameter values for multiple observation points. We retrieve optimal drag coefficients of CA=1.2×10−3 and CO=2.4×10−3 from 10‐day averaged Arctic‐wide data from July 2014 that agree with the AIDJEX standard, with clear temporal and spatial variations. Inverting daily averaged buoy data give parameters that, while more accurately resolved, suggest that the forward model oversimplifies the physical system at these spatial and temporal scales. Our results show the importance of the correct representation of geostrophic currents. Both atmospheric and oceanic drag coefficients are found to decrease with shorter temporal averaging period, informing the selection of drag coefficient for short timescale climate models.
    Description: The scripts developed for this publication are available at the GitHub (https://github.com/hheorton/Freedrift_inverse_submit). The Neighborhood Algorithm was developed and kindly supplied by M. Sambridge (http://www.iearth.org.au/codes/NA/). Ice‐Tethered Profiler data are available via the Ice‐Tethered Profiler program website (http://whoi.edu/itp). Buoy data were collected as part of the Marginal Ice Zone program (www.apl.washington.edu/miz) funded by the U.S. Office of Naval Research. The ice drift data were kindly supplied by N. Kimura. H. H. was funded by the Natural Environment Research Council (Grants NE/I029439/1 and NE/R000263/1). M. T. was partially funded by the SKIM Mission Science Study (SKIM‐SciSoc) Project ESA RFP 3‐15456/18/NL/CT/gp. T. A. was supported at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. M. T. and H. H. thank Dr. Nicolas Brantut for early discussions on the implementation of inverse modeling techniques.
    Description: 2020-02-14
    Keywords: Sea ice drift ; Observations ; Inverse modeling ; Drag coefficients
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 6
    Publication Date: 2022-10-26
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Proshutinsky, A., Krishfield, R., Toole, J. M., Timmermans, M-L., Williams, W. J., Zimmermann, S., Yamamoto-Kawai, M., Armitage, T. W. K., Dukhovskoy, D., Golubeva, E., Manucharyan, G. E., Platov, G., Watanabe, E., Kikuchi, T., Nishino, S., Itoh, M., Kang, S-H., Cho, K-H., Tateyama, K., & Zhao, J. Analysis of the Beaufort Gyre freshwater content in 2003-2018. Journal of Geophysical Research-Oceans, 124(12), (2019): 9658-9689, doi:10.1029/2019JC015281.
    Description: Hydrographic data collected from research cruises, bottom‐anchored moorings, drifting Ice‐Tethered Profilers, and satellite altimetry in the Beaufort Gyre region of the Arctic Ocean document an increase of more than 6,400 km3 of liquid freshwater content from 2003 to 2018: a 40% growth relative to the climatology of the 1970s. This fresh water accumulation is shown to result from persistent anticyclonic atmospheric wind forcing (1997–2018) accompanied by sea ice melt, a wind‐forced redirection of Mackenzie River discharge from predominantly eastward to westward flow, and a contribution of low salinity waters of Pacific Ocean origin via Bering Strait. Despite significant uncertainties in the different observations, this study has demonstrated the synergistic value of having multiple diverse datasets to obtain a more comprehensive understanding of Beaufort Gyre freshwater content variability. For example, Beaufort Gyre Observational System (BGOS) surveys clearly show the interannual increase in freshwater content, but without satellite or Ice‐Tethered Profiler measurements, it is not possible to resolve the seasonal cycle of freshwater content, which in fact is larger than the year‐to‐year variability, or the more subtle interannual variations.
    Description: National Science Foundation. Grant Numbers: PLR‐1302884,OPP‐1719280, and OPP‐1845877, PLR‐1303644 and OPP‐1756100, OPP‐1756100, PLR‐1303644, OPP‐1845877, OPP‐1719280, PLR‐1302884 Key Program of National Natural Science Foundation of China. Grant Number: 41330960 Global Change Research Program of China. Grant Number: 2015CB953900 Ministry of Education, Korea Japan Aerospace Exploration Agency (JAXA) /Earth Observation Research Center (EORC) Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT) Stanback Postdoctoral Fellowship Russian Foundation for Basic Research. Grant Number: 17‐05‐00382 Presidium of Russian Academy of Sciences HYCOM NOPP. Grant Number: N00014‐15‐1‐2594 DOE. Grant Number: DE‐SC0014378 National Aeronautics and Space Administration Tokyo University of Marine Science and Technology Department of Fisheries and Oceans Canada Woods Hole Oceanographic Institution
    Keywords: Beaufort Gyre ; Arctic Ocean ; Freshwater balance ; Circulation ; Modeling ; Climate change
    Repository Name: Woods Hole Open Access Server
    Type: Article
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