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  • 1
    Online Resource
    Online Resource
    Milton :Taylor & Francis Group,
    Keywords: Artificial intelligence. ; Electronic books.
    Description / Table of Contents: 5G-Based Smart Hospitals and Healthcare Systems provides an overview of the role of advanced technologies in transforming the healthcare industry. It emphasizes the technical requirements of smart hospitals, explaining how technologies such as IoT, machine learning, and AI can be integrated with smart hospitals and 5G networks.
    Type of Medium: Online Resource
    Pages: 1 online resource (267 pages)
    Edition: 1st ed.
    ISBN: 9781003823827
    Series Statement: Advancements in Intelligent and Sustainable Technologies and Systems Series
    DDC: 362.110285
    Language: English
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  • 2
    Online Resource
    Online Resource
    Singapore :Springer,
    Keywords: Climatic changes. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (318 pages)
    Edition: 1st ed.
    ISBN: 9789811603945
    Series Statement: Springer Transactions in Civil and Environmental Engineering Series
    DDC: 333.91
    Language: English
    Note: Intro -- Foreword -- Preface -- Contents -- About the Editors -- 1 Climate Change, and Water and Food Security: Policies Within Water-Food-Energy Nexus -- 1.1 Introduction -- 1.2 US Midwest Case Studies on Climate Change Effects on Water and Food Security -- 1.3 India Case Study on Climate Change Effects on Food and Water Security in South Asia -- 1.4 Conclusions -- References -- 2 Climate Change Risks to Water Security in Canada's Western Interior -- 2.1 Introduction -- 2.1.1 Natural Variability of the Regional Hydroclimatic -- 2.2 Climate and Hydrology of the Upper North Saskatchewan River Basin -- 2.2.1 Climate -- 2.2.2 Hydrology -- 2.2.3 The Paleohydrology of the NSRB -- 2.3 Water Use and Demand -- 2.3.1 Historical Water Use -- 2.3.2 Projected Water Demand -- 2.4 The Future Climate of the NSRB -- 2.5 Hydrological Response to Projected Climate Changes -- 2.5.1 Hydrological Modelling of the NSRB Above Edmonton -- 2.5.2 Calibration and Validation of the MESH Model -- 2.5.3 Streamflow Projections -- 2.6 Discussion -- References -- 3 Forcing of Global Hydrological Changes in the Twentieth and Twenty-First Centuries -- 3.1 Introduction -- 3.2 Aerosol Radiative Forcing -- 3.3 Twentieth Century Climate Response to Forcings -- 3.4 Twenty-First Century Climate Response to Forcings -- 3.5 Summary and Conclusions -- References -- 4 Indian Summer Monsoon System: A Holistic Approach for Advancing Monsoon Understanding in a Warming World -- 4.1 Introduction -- 4.1.1 ISMS Components and Global Warming: Gaps in Knowledge -- 4.2 Summary -- References -- 5 Observed Climate Change Over India and Its Impact on Hydrological Sectors -- 5.1 Introduction -- 5.2 Data and Methodology -- 5.3 Results and Discussions -- 5.3.1 All India Mean Temperature Trend -- 5.3.2 All India Maximum Temperature Trend -- 5.3.3 All India Minimum Temperature Trend. , 5.3.4 Spatial Pattern of Temperature Trend -- 5.3.5 Rainfall Patterns Over Major River Basins -- 5.3.6 Rainfall Variability Over Major River Basins -- 5.3.7 Rainfall Trends Over Major River Basins -- 5.4 Conclusion -- References -- 6 Importance of Data in Mitigating Climate Change -- 6.1 Introduction: Climate Change and Water Security -- 6.2 Water Resources in India -- 6.3 Usage of Data-Based Tools for Assessment of Climate Impacts on Water Security -- 6.3.1 Role of Data in Water Development, Management and Ensuring Security -- 6.3.2 Data for Water Diplomacy -- 6.3.3 Reasonability of Demand and Dynamic Water Availability -- 6.3.4 Ensuring Water Security Through Budgeting and Accounting -- 6.4 Data Collection -- 6.5 Role of Data in Project Planning and Management to Make Them Resilient to Increasing Variability of Climate -- 6.6 Current Challenges and Data Requirement to Address the Challenges for Water Sector -- 6.6.1 Current and Future Scenario of Water Availability and Demand -- 6.7 Limitations and Resolutions for Data Collection and Processing -- 6.8 Data-Based Governance -- 6.9 Conclusions and Way Forward -- 6.9.1 Conclusions -- 6.9.2 Way Forward -- References -- 7 Real-Time Monitoring of Small Reservoir Hydrology Using ICT and Application of Deep Learning for Prediction of Water Level -- 7.1 Introduction -- 7.2 Material and Methods -- 7.2.1 Study Site -- 7.2.2 Water Level and Weather Data Monitoring -- 7.2.3 Water Level Prediction Model Using LSTM -- 7.2.4 Layer Setting for LSTM Model Development -- 7.2.5 LSTM Model Evaluation -- 7.2.6 Reservoir Monitoring System and Water Level Prediction Model -- 7.3 Results and Discussion -- 7.3.1 Collection and Display of Water Level and Weather Data -- 7.3.2 Reservoir Monitoring by Web Camera -- 7.3.3 Water Level Prediction Component of the System. , 7.3.4 Visualization of Past, Present, and Future Water Level and Rainfall -- 7.4 Conclusions -- References -- 8 Hydrology: Problems, Challenges and Opportunities -- 8.1 Introduction -- 8.2 Anatomy of Hydrology -- 8.3 Grand Challenges -- 8.4 A Glimpse of History of Hydrology -- 8.5 Evolution of Hydrologic Models -- 8.6 Recent Advances -- 8.6.1 Data Collection and Processing Technology -- 8.6.2 Spatial Hydrologic Variability -- 8.6.3 Scaling and Variability -- 8.6.4 Model Calibration -- 8.6.5 Emerging Tools -- 8.7 Hydrologic Modelling Challenges -- 8.7.1 Environmental Sustainability -- 8.7.2 Modelling Challenges -- 8.8 Future Outlook -- 8.9 Reexamination and Reflection -- 8.10 Summation -- References -- 9 Flood Modelling, Mapping and Monitoring of Sparsely Gauged Catchments Using Remote Sensing Products -- 9.1 Introduction -- 9.2 Case Study -- 9.3 Global Datasets -- 9.3.1 Digital Elevation Data -- 9.3.2 Rainfall Data -- 9.3.3 Other Data -- 9.4 Methodology -- 9.4.1 Bias Correction -- 9.4.2 Hydrological Modelling -- 9.4.3 Hydraulic Modelling -- 9.4.4 Uncertainty Assessment -- 9.5 Results and Discussion -- 9.5.1 Bias Correction -- 9.5.2 Hydrological Modelling -- 9.5.3 Hydraulic Modelling -- 9.5.4 Uncertainty Assessment -- 9.6 Conclusions and Recommendations -- References -- 10 Ground and Satellite Observations to Predict Flooding Phenomena -- 10.1 Introduction -- 10.2 Soil Moisture Monitoring and Antecedent Wetness Conditions -- 10.3 Discharge Monitoring and Advanced Technology for Flood Prediction -- 10.3.1 Evaluating the Potential for Measuring River Discharge from Space -- 10.4 Conclusions -- References -- 11 Indices for Meteorological and Hydrological Drought -- 11.1 Overview -- 11.1.1 Drought Forms -- 11.1.2 The Metric for Deficiency -- 11.1.3 Timescale Considerations -- 11.1.4 The Drought Index Approach -- 11.2 Meteorological Drought Indices. , 11.2.1 Palmer Drought Severity Index (PDSI) -- 11.2.2 Rainfall Deciles -- 11.2.3 Standardized Precipitation Index (SPI) -- 11.2.4 Standardized Precipitation Evapotranspiration Index (SPEI) -- 11.3 Hydrological Drought Indices -- 11.3.1 Total Water Deficit -- 11.3.2 Surface Water Supply Index (SWSI) -- 11.4 Composite Drought Indices -- 11.4.1 U.S. Drought Monitor (USDM) -- 11.4.2 Aggregate Drought Index (ADI) -- References -- 12 Water Resources Management-An Indian Perspective -- 12.1 Background -- 12.1.1 Water Availability -- 12.1.2 Water Demand -- 12.1.3 Use of Water for Irrigation -- 12.1.4 Irrigation Potential Created (IPC) and Irrigation Potential Utilized (IPU) -- 12.1.5 Water Use Efficiency -- 12.1.6 Participatory Irrigation Management -- 12.1.7 Traditional Water Harvesting Systems -- 12.2 Traditional Irrigation Systems -- 12.3 Green Revolution in India -- 12.3.1 Water Scenario Post the Green Revolution -- 12.4 Present-Day Relevance of Traditional Water Conservation Practices -- 12.4.1 Jal Mandir (Gujarat) -- 12.4.2 Khatri, Kuhl (H.P., J& -- K) -- 12.4.3 Zabo (Nagaland) -- 12.4.4 Eri, Ooranis (T.N.) -- 12.4.5 Dongs (Assam) -- 12.4.6 Katas, Mundas and Bandhas (Odisha and M.P.) -- 12.4.7 Surangam (Kerala) -- 12.4.8 Bawdi /Jhalara (Gujarat /Rajasthan /Karnataka) -- 12.5 Water Management Initiatives in Recent Times -- 12.6 Initiatives by Various States -- 12.6.1 Sujalam Sufalam Jal Abhiyan (SSJA) in Gujarat -- 12.6.2 Mukhyamantri Jal Swavlamban Abhiyan of Rajasthan State -- 12.6.3 Pani Bachao, Paise Kamao in Punjab -- 12.6.4 Mera Pani Meri Virasat in Haryana -- 12.7 Participatory Irrigation Management (Pim) and Initiatives by Communities and Individuals -- 12.7.1 Ralegan Siddhi -- 12.7.2 Hiware Bazar -- 12.8 Efforts Towards Water Conservation-Initiatives by Central Government -- 12.8.1 Pradhan Mantri Krishi Sinchayi Yojana (PMKSY). , 12.8.2 Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), 2005 -- 12.8.3 Jal Shakti Abhiyan -- 12.8.4 Catch the Rain -- 12.9 Conclusion -- References -- 13 Overview of Water Resources Management in India -- 13.1 Introduction -- 13.2 Challenges in Water Management -- 13.3 Water Sector Governance in India -- 13.4 Inter-state and Other Conflicts in Water Sector -- 13.5 Financial sustainability of Water Resources Development -- 13.6 Conclusion -- References -- 14 Adaptation to Climate Change in Agriculture: An Exploration of Technology and Policy Options in India -- 14.1 Introduction -- 14.1.1 Climate Change Impact on Agriculture -- 14.2 Understanding the Adaptation -- 14.2.1 Classification of Adaptations -- 14.2.2 Strategies for Adaptation Planning -- 14.2.3 Adaptations in Agriculture Are Water-Centric -- 14.3 Vulnerability Mapping of Indian Agriculture -- 14.3.1 Quantitative Estimation of Vulnerability -- 14.4 Adaptation Actions in India -- 14.4.1 Case-I: Adaptation and Adaptation-Led Mitigation with Green Revolution Technologies -- 14.4.2 Case-II: Adaptation Through Diversification and Climate-Smart Technologies -- 14.4.3 Risk Transfer as Mechanism for Promoting Adaptation -- 14.5 Policies and Institutions -- 14.5.1 Green Revolution Period Policies -- 14.5.2 Post-Green Revolution Policies -- 14.6 Concluding Remarks and Way Forward -- References -- 15 Adapting Improved Agricultural Water Management and Protected Cultivation Technologies-Strategic Dealing with Climate Change Challenge -- 15.1 Introduction -- 15.1.1 The Essence of Food Production -- 15.1.2 Climate Change and Agriculture -- 15.2 Climate Change Effects on Cultivation Practices -- 15.3 Crop Variety and Sowing Time -- 15.3.1 Crop Disease and Pests Management -- 15.4 Agricultural Water Management and Climate Change -- 15.4.1 Improved Irrigation Techniques. , 15.5 Protected Cultivation Technologies.
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  • 3
    Publication Date: 2019-04-01
    Keywords: ddc:600
    Repository Name: Wuppertal Institut für Klima, Umwelt, Energie
    Language: English
    Type: bookpart , doc-type:bookPart
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  • 4
    Publication Date: 2021-06-30
    Description: Western North Pacific tropical cyclone (TC) model tracks are analyzed in two large multimodelensembles, spanning a large variety of models and multiple future climate scenarios. Two methodologiesare used to synthesize the properties of TC tracks in this large data set: cluster analysis and mass momentellipses. First, the models’ TC tracks are compared to observed TC tracks’ characteristics, and a subset ofthe models is chosen for analysis, based on the tracks’ similarity to observations and sample size. Potentialchanges in track types in a warming climate are identified by comparing the kernel smoothed probabilitydistributions of various track variables in historical and future scenarios using a Kolmogorov-Smirnovsignificance test. Two track changes are identified. The first is a statistically significant increase in thenorth-south expansion, which can also be viewed as a poleward shift, as TC tracks are prevented fromexpanding equatorward due to the weak Coriolis force near the equator. The second change is an eastwardshift in the storm tracks that occur near the central Pacific in one of the multimodel ensembles, indicatinga possible increase in the occurrence of storms near Hawaii in a warming climate. The dependence of theresults on which model and future scenario are considered emphasizes the necessity of including multiplemodels and scenarios when considering future changes in TC characteristics.
    Description: Published
    Description: 9721–9744
    Description: 4A. Oceanografia e clima
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 5
    Publication Date: 2022-05-25
    Description: Author Posting. © American Meteorological Society, 2017. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 30 (2017): 3829-3852, doi:10.1175/JCLI-D-16-0479.1.
    Description: This study provides an assessment of the uncertainty in ocean surface (OS) freshwater budgets and variability using evaporation E and precipitation P from 10 atmospheric reanalyses, two combined satellite-based E − P products, and two observation-based salinity products. Three issues are examined: the uncertainty level in the OS freshwater budget in atmospheric reanalyses, the uncertainty structure and association with the global ocean wet/dry zones, and the potential of salinity in ascribing the uncertainty in E − P. The products agree on the global mean pattern but differ considerably in magnitude. The OS freshwater budgets are 129 ± 10 (8%) cm yr−1 for E, 118 ± 11 (9%) cm yr−1 for P, and 11 ± 4 (36%) cm yr−1 for E − P, where the mean and error represent the ensemble mean and one standard deviation of the ensemble spread. The E − P uncertainty exceeds the uncertainty in E and P by a factor of 4 or more. The large uncertainty is attributed to P in the tropical wet zone. Most reanalyses tend to produce a wider tropical rainband when compared to satellite products, with the exception of two recent reanalyses that implement an observation-based correction for the model-generated P over land. The disparity in the width and the extent of seasonal migrations of the tropical wet zone causes a large spread in P, implying that the tropical moist physics and the realism of tropical rainfall remain a key challenge. Satellite salinity appears feasible to evaluate the fidelity of E − P variability in three tropical areas, where the uncertainty diagnosis has a global indication.
    Description: Primary support for the study is provided by the NOAAModeling, Analysis, Predictions, and Projections (MAPP) Program’s Climate Reanalysis Task Force (CRTF) through Grant NA13OAR4310106.
    Description: 2017-11-02
    Keywords: Hydrologic cycle ; Precipitation ; Evaporation ; Salinity ; Water budget ; Reanalysis data
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 6
    Publication Date: 2022-05-25
    Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jacox, M. G., Alexander, M. A., Siedlecki, S., Chen, K., Kwon, Y., Brodie, S., Ortiz, I., Tommasi, D., Widlansky, M. J., Barrie, D., Capotondi, A., Cheng, W., Di Lorenzo, E., Edwards, C., Fiechter, J., Fratantoni, P., Hazen, E. L., Hermann, A. J., Kumar, A., Miller, A. J., Pirhalla, D., Buil, M. P., Ray, S., Sheridan, S. C., Subramanian, A., Thompson, P., Thorne, L., Annamalai, H., Aydin, K., Bograd, S. J., Griffis, R. B., Kearney, K., Kim, H., Mariotti, A., Merrifield, M., & Rykaczewski, R. Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments. Progress in Oceanography, 183, (2020): 102307, doi:10.1016/j.pocean.2020.102307.
    Description: Marine ecosystem forecasting is an area of active research and rapid development. Promise has been shown for skillful prediction of physical, biogeochemical, and ecological variables on a range of timescales, suggesting potential for forecasts to aid in the management of living marine resources and coastal communities. However, the mechanisms underlying forecast skill in marine ecosystems are often poorly understood, and many forecasts, especially for biological variables, rely on empirical statistical relationships developed from historical observations. Here, we review statistical and dynamical marine ecosystem forecasting methods and highlight examples of their application along U.S. coastlines for seasonal-to-interannual (1–24 month) prediction of properties ranging from coastal sea level to marine top predator distributions. We then describe known mechanisms governing marine ecosystem predictability and how they have been used in forecasts to date. These mechanisms include physical atmospheric and oceanic processes, biogeochemical and ecological responses to physical forcing, and intrinsic characteristics of species themselves. In reviewing the state of the knowledge on forecasting techniques and mechanisms underlying marine ecosystem predictability, we aim to facilitate forecast development and uptake by (i) identifying methods and processes that can be exploited for development of skillful regional forecasts, (ii) informing priorities for forecast development and verification, and (iii) improving understanding of conditional forecast skill (i.e., a priori knowledge of whether a forecast is likely to be skillful). While we focus primarily on coastal marine ecosystems surrounding North America (and the U.S. in particular), we detail forecast methods, physical and biological mechanisms, and priority developments that are globally relevant.
    Description: This study was supported by the NOAA Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) program through grants NA17OAR4310108, NA17OAR4310112, NA17OAR4310111, NA17OAR4310110, NA17OAR4310109, NA17OAR4310104, NA17OAR4310106, and NA17OAR4310113. This paper is a product of the NOAA/MAPP Marine Prediction Task Force.
    Keywords: Prediction ; Predictability ; Forecast ; Ecological forecast ; Mechanism ; Seasonal ; Interannual ; Large marine ecosystem
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 7
    Publication Date: 2019-04-01
    Keywords: ddc:600
    Repository Name: Wuppertal Institut für Klima, Umwelt, Energie
    Language: English
    Type: bookpart , doc-type:bookPart
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  • 8
    Publication Date: 2022-05-26
    Description: Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Climate Dynamics 49 (2017): 3327–3344, doi:10.1007/s00382-016-3516-6.
    Description: NCEP/DOE reanalysis (R2) and Climate Forecast System Reanalysis (CFSR) surface fluxes are widely used by the research community to understand surface flux climate variability, and to drive ocean models as surface forcings. However, large discrepancies exist between these two products, including (1) stronger trade winds in CFSR than in R2 over the tropical Pacific prior 2000; (2) excessive net surface heat fluxes into ocean in CFSR than in R2 with an increase in difference after 2000. The goals of this study are to examine the sensitivity of ocean simulations to discrepancies between CFSR and R2 surface fluxes, and to assess the fidelity of the two products. A set of experiments, where an ocean model was driven by a combination of surface flux component from R2 and CFSR, were carried out. The model simulations were contrasted to identify sensitivity to different component of the surface fluxes in R2 and CFSR. The accuracy of the model simulations was validated against the tropical moorings data, altimetry SSH and SST reanalysis products. Sensitivity of ocean simulations showed that temperature bias difference in the upper 100m is mostly sensitive to the differences in surface heat fluxes, while depth of 20°C (D20) bias difference is mainly determined by the discrepancies in momentum fluxes. D20 simulations with CFSR winds agree with observation well in the western equatorial Pacific prior 2000, but have large negative bias similar to those with R2 winds after 2000, partly because easterly winds over the central Pacific were underestimated in both CFSR and R2. On the other hand, the observed temperature variability is well reproduced in the tropical Pacific by simulations with both R2 and CFSR fluxes. Relative to the R2 fluxes, the CFSR fluxes improve simulation of interannual variability in all three tropical oceans to a varying degree. The improvement in the tropical Atlantic is most significant and is largely attributed to differences in surface winds.
    Keywords: CFSR ; NCEP/DOE reanalysis (R2) ; Surface wind stress/heat flux validation ; Ocean model ; Tropical moored buoy data
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
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  • 9
    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 Subramanian, A. C., Balmaseda, M. A., Centurioni, L., Chattopadhyay, R., Cornuelle, B. D., DeMott, C., Flatau, M., Fujii, Y., Giglio, D., Gille, S. T., Hamill, T. M., Hendon, H., Hoteit, I., Kumar, A., Lee, J., Lucas, A. J., Mahadevan, A., Matsueda, M., Nam, S., Paturi, S., Penny, S. G., Rydbeck, A., Sun, R., Takaya, Y., Tandon, A., Todd, R. E., Vitart, F., Yuan, D., & Zhang, C. Ocean observations to improve our understanding, modeling, and forecasting of subseasonal-to-seasonal variability. Frontiers in Marine Science, 6, (2019): 427, doi:10.3389/fmars.2019.00427.
    Description: Subseasonal-to-seasonal (S2S) forecasts have the potential to provide advance information about weather and climate events. The high heat capacity of water means that the subsurface ocean stores and re-releases heat (and other properties) and is an important source of information for S2S forecasts. However, the subsurface ocean is challenging to observe, because it cannot be measured by satellite. Subsurface ocean observing systems relevant for understanding, modeling, and forecasting on S2S timescales will continue to evolve with the improvement in technological capabilities. The community must focus on designing and implementing low-cost, high-value surface and subsurface ocean observations, and developing forecasting system capable of extracting their observation potential in forecast applications. S2S forecasts will benefit significantly from higher spatio-temporal resolution data in regions that are sources of predictability on these timescales (coastal, tropical, and polar regions). While ENSO has been a driving force for the design of the current observing system, the subseasonal time scales present new observational requirements. Advanced observation technologies such as autonomous surface and subsurface profiling devices as well as satellites that observe the ocean-atmosphere interface simultaneously can lead to breakthroughs in coupled data assimilation (CDA) and coupled initialization for S2S forecasts. These observational platforms should also be tested and evaluated in ocean observation sensitivity experiments with current and future generation CDA and S2S prediction systems. Investments in the new ocean observations as well as model and DA system developments can lead to substantial returns on cost savings from disaster mitigation as well as socio–economic decisions that use S2S forecast information.
    Description: AS was funded by NOAA Climate Variability and Prediction Program (NA14OAR4310276) and the NSF Earth System Modeling Program (OCE1419306). CD was funded by NA16OAR4310094. SG and DG were funded by NASA awards NNX14AO78G and 80NSSC19K0059. DY was supported by NSFC (91858204, 41720104008, and 41421005).
    Keywords: Subseasonal ; Seasonal ; Predictions ; Air-sea interaction ; Satellite ; Argo ; Gliders ; Drifters
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 10
    ISSN: 1520-5002
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Type of Medium: Electronic Resource
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