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  • 1
    Online Resource
    Online Resource
    Singapore :Springer Singapore Pte. Limited,
    Keywords: Ecology-Russia (Federation). ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (310 pages)
    Edition: 1st ed.
    ISBN: 9789811363177
    Series Statement: Ecological Studies ; v.236
    DDC: 363.700947
    Language: English
    Note: Intro -- Preface -- Contents -- Chapter 1: Water and Carbon Dynamics in Eastern Siberia: Introduction -- 1.1 Climate, Permafrost, and Vegetation -- 1.2 Climate of the Boreal Forest and Tundra -- 1.2.1 Formation of the Boreal Forest -- 1.2.2 Formation of Forest/Tundra Vegetation -- 1.3 Water and Carbon Dynamics in Eastern Siberia During the Former Soviet Union Period -- 1.3.1 Hydrological Processes Studies in the Soviet Union -- 1.3.1.1 Outline of Hydrological Cycles -- 1.3.1.2 Depth of Active Layer -- 1.3.1.3 Evaporation and Transpiration -- 1.3.1.4 Water Balance -- 1.3.2 Carbon Dioxide Cycles in the Soviet Union -- 1.3.2.1 Outline of Carbon Dioxide Cycles -- 1.3.2.2 Microbiology in Permafrost -- 1.3.3 Permafrost Dynamics Studies During the Soviet Period -- 1.3.3.1 Outline of Permafrost Dynamics -- 1.3.3.2 Observational Studies of Permafrost -- 1.3.3.3 Modelling Investigations of Permafrost -- 1.3.3.4 Monitoring Observations in Permafrost -- 1.3.4 Soil Investigation for the Soviet Union -- 1.4 Concluding Remarks -- References -- Chapter 2: Atmospheric Water Cycle -- 2.1 Introduction -- 2.2 Climatological Water Budget -- 2.3 Seasonal Cycle -- 2.4 Moisture Transport -- 2.5 Origin of Precipitating Water and Recycling -- 2.6 Seasonal Time Lag Between P-ET and R -- 2.7 Interannual Variation -- 2.8 Concluding Remarks -- References -- Chapter 3: Water Cycles in Forests -- 3.1 Introduction -- 3.2 Study Area -- 3.3 Evapotranspiration of the Larch Forest in Eastern Siberia -- 3.3.1 Seasonal Variation of the Forest Evapotranspiration -- 3.3.2 Evapotranspiration from the Understory Vegetation -- 3.3.3 Interception Evaporation -- 3.3.4 Forest Water Balance -- 3.4 Response of the Forest to Environmental Conditions -- 3.4.1 Evapotranspiration -- 3.4.2 Conductance -- 3.5 Spatial Variability in Eastern Siberia -- 3.6 Response of Larch Forests to Wetting Climates. , 3.7 Concluding Remarks -- References -- Chapter 4: Carbon Cycles in Forests -- 4.1 Introduction -- 4.2 Photosynthetic Activity of Larch Forests -- 4.2.1 Diurnal Dynamics of Photosynthesis -- 4.2.2 Seasonal Dynamics of Photosynthesis -- 4.2.3 The Maximum Intensity of Photosynthesis (Amax) -- 4.2.4 Ratio of Photosynthesis to Dark Respiration (Rdark) of Plants -- 4.2.5 Light Dependence of Photosynthesis -- 4.2.6 Nitrogen and Nutrients in a Larch Forest -- 4.2.7 Assessment of the Biochemical Parameters that Limit Photosynthesis -- 4.3 Soil Respiration in a Larch Forest -- 4.3.1 Daily Dynamics of Soil Respiration -- 4.3.2 Seasonal Dynamics of Soil Respiration -- 4.3.3 Interannual and Spatial Variation in Soil Respiration -- 4.3.4 Environmental Dependencies of Soil Respiration -- 4.4 NEE of CO2 in Larch Forest -- 4.4.1 The Daily and Seasonal Dynamics -- 4.4.2 Contribution of Permafrost Forest in the Terrestrial Carbon Cycle of Russia -- 4.5 Concluding Remarks -- References -- Chapter 5: Methane and Biogenic Volatile Organic Compound Emissions in Eastern Siberia -- 5.1 Introduction -- 5.2 The Ecosystem CH4 Source -- 5.2.1 Data Uncertainty -- 5.2.2 Processes -- 5.2.3 Terrestrial Ecosystems: Spatial and Seasonal Variation -- 5.2.4 The Ecosystem CH4 Source in Ponds and Lakes -- 5.3 Old Soil Carbon as a Source of CH4 -- 5.4 Deep Permafrost CH4 Sources -- 5.5 Effects of Environmental Change -- 5.5.1 Climate Change -- 5.5.2 Direct Climate Warming Effects -- 5.5.3 Geomorphological Change -- 5.5.4 Other Anthropogenic Disturbances -- 5.6 BVOC -- 5.7 Conclusions -- References -- Chapter 6: Stable Isotopes of Water in Permafrost Ecosystem -- 6.1 Introduction -- 6.1.1 Moisture in Permafrost Ecosystem -- 6.1.2 Use of Stable Isotopes of Water -- 6.2 Water Budget of Taiga Forest Ecosystem -- 6.2.1 Changes in Soil Moisture and Its Water Isotopic Composition. , 6.2.1.1 Isotopic Composition of Precipitation in Eastern Siberia -- 6.2.1.2 Soil Moisture Equivalent and the Water Isotopes -- 6.2.2 Source of Water for Plants -- 6.2.3 Role of Water and Ice in the Bottom Layer of Active Layer and Uppermost Layer of Permafrost -- 6.2.4 Discharge of Water from Land to River -- 6.3 Taiga as a Source of Atmospheric Water Vapor -- 6.4 Concluding Remarks -- References -- Chapter 7: Water-Carbon Cycle in Dendrochronology -- 7.1 Introduction -- 7.2 Stable Carbon Isotope in Tree Rings -- 7.2.1 Analysis and Theory -- 7.2.2 Corrections -- 7.2.3 Intrinsic Water-Use Efficiency -- 7.3 Analysis of Tree Physiological Response to Past Climate Change -- 7.3.1 Study Sites -- 7.3.2 Positive Tree Growth Response to Warming in a Subarctic Forest Ecosystem -- 7.3.3 Negative Tree Growth Response to Warming in Southern Boreal Forests -- 7.3.4 Challenges for the Future Development of Tree-Ring Carbon Cycling Research -- 7.4 Reconstruction of Past Climate and Environmental Changes -- 7.4.1 Hydroclimatic Reconstruction Based on Larch Tree-Ring δ13C -- 7.4.2 Reconstruction of In Situ Soil Moisture Observational Record Based on Larch Tree-Ring δ13C -- 7.5 Conclusion Remarks -- Reference -- Chapter 8: Permafrost-Forest Dynamics -- 8.1 Introduction -- 8.2 Permafrost-Related Landscape -- 8.2.1 Geological History of Permafrost Evolution -- 8.2.2 Yedoma and Alas Formation -- 8.2.3 Recent Thermokarst Depression -- 8.2.4 Mountain Permafrost -- 8.3 Permafrost Structure (Profile, Ice) -- 8.3.1 Ice Complex (Yedoma) Distribution in Lena-Aldan Interfluve -- 8.3.2 Shielding Layer -- 8.4 Permafrost Temperature Change -- 8.4.1 Long-Term Changes in Northern Eurasia -- 8.4.2 Long-Term Changes in Eastern Siberia -- 8.5 Active-Layer Thickness Change -- 8.5.1 Russia -- 8.5.2 Central Yakutia -- 8.6 Permafrost Degradation in Forests (Forest Fires, Wetting). , 8.6.1 Forest Fire -- 8.6.2 Clear-Cutting -- 8.6.3 Wet Climate -- 8.7 Permafrost Degradation in Grassland (Thermokarst, Alas) -- 8.7.1 Evolution of Yedoma and Thermokarst Lakes -- 8.7.2 Emerging Degradation in Dry Grassland (Churapcha) -- 8.8 Future Climate Projection -- 8.9 Concluding Remarks -- References -- Chapter 9: River Discharge -- 9.1 Introduction -- 9.2 Lena River Basin -- 9.2.1 Geographical Scope -- 9.2.2 Seasonal Changes in Lena River Discharge -- 9.2.3 Long-Term Trend of Lena River Discharge -- 9.3 Hydrological Modeling for Arctic River Discharge -- 9.3.1 River Runoff Modeling -- 9.3.2 River Ice Modeling -- 9.3.3 Future Projections -- 9.4 River Water Chemistry in the Arctic -- 9.4.1 Importance of River Water Chemistry in the Arctic -- 9.4.2 Monitoring of River Water Chemistry in the Arctic -- 9.5 Concluding Remarks -- References -- Chapter 10: Remote Sensing of Vegetation -- 10.1 Introduction -- 10.2 Observation of Aboveground Biomass -- 10.2.1 Vegetation Indices -- 10.2.2 Leaf Area Index -- 10.2.3 Radar and LiDAR Remote Sensing -- 10.3 Observation of Plant Functional Type -- 10.4 Observations of Growing Season Duration -- 10.4.1 Satellite Observation -- 10.4.2 Satellite Observation Issues -- 10.4.2.1 Issue 1: Systematic Noise -- 10.4.2.2 Issue 2: Atmospheric Noise and Cloud Contamination -- 10.4.2.3 Issue 3: Heterogeneity of Plant Functional Type -- 10.4.2.4 Issue 4: Effect of Solar Zenith and View Angles -- 10.4.2.5 Issue 5: Insufficient Ground-Truthing -- 10.4.3 In Situ Observations -- 10.4.4 Integrating In Situ and Satellite Observations -- 10.5 Concluding Remarks -- References -- Chapter 11: Remote Sensing of Terrestrial Water -- 11.1 Introduction -- 11.2 Terrestrial Water Storage (TWS) -- 11.2.1 Remote Sensing of TWS -- 11.2.2 TWS Datasets -- 11.2.3 Scientific Applications of TWS -- 11.2.4 Perspectives on TWS Remote Sensing. , 11.3 Soil Moisture (SM) -- 11.3.1 Remote Sensing of SM -- 11.3.2 SM Datasets -- 11.3.2.1 ASCAT -- 11.3.2.2 AMSR2 -- 11.3.2.3 SMOS -- 11.3.2.4 SMAP -- 11.3.2.5 Land Parameter Retrieval Model (LPRM) -- 11.3.3 Scientific Applications of SM -- 11.3.4 Perspectives on SM Remote Sensing -- 11.4 Snow -- 11.4.1 Remote Sensing of Snow -- 11.4.2 Snow Datasets -- 11.4.2.1 Snow Coverage Area -- 11.4.2.1.1 MODIS -- 11.4.2.1.2 IMS (Interactive Multisensor Snow and Ice Mapping System) -- 11.4.2.1.3 NOAA Weekly Data -- 11.4.2.2 Snow Water Equivalent (SWE) -- 11.4.2.2.1 Passive Microwave Instruments (SSM/I and AMSR2) (Foster et al. 2005 -- Tedesco and Jeyaratnam 2016) -- 11.4.2.2.2 GlobSnow, European Space Agency (ESA) -- 11.4.3 Scientific Applications of Snow -- 11.4.4 Perspectives on Snow Remote Sensing -- 11.5 Surface Water (SW) -- 11.5.1 Remote Sensing of SW -- 11.5.2 SW Datasets -- 11.5.2.1 Landsat-TM -- 11.5.2.2 AMSR-E/AMSR2 -- 11.5.2.3 Multiple Sensors -- 11.5.3 Scientific Applications of SW -- 11.5.4 Perspectives on SW Remote Sensing -- 11.6 Emerging Research: Data Assimilation -- 11.7 Concluding Remarks -- References -- Chapter 12: Carbon-Water Cycle Modeling -- 12.1 Introduction -- 12.2 Model Assessment at Site-Specific Scales -- 12.2.1 Model Description -- 12.2.2 Water and Energy Balance -- 12.2.3 Rapid Increases in Soil Temperature and Moisture -- 12.3 Pan-Arctic Model Simulation -- 12.3.1 Model Description -- 12.3.2 Increasing Snow in Siberia -- 12.3.3 Warming and Permafrost Degradation -- 12.3.4 Changes in Hydrologic Processes in Eastern Siberia -- 12.3.5 Changes in Carbon Fluxes in Eastern Siberia -- 12.4 Concluding Remarks -- References -- Chapter 13: Water and Carbon Dynamics in Eastern Siberia: Concluding Remarks -- 13.1 Introduction -- 13.2 Main Results in the Water and Carbon Dynamics in Eastern Siberia -- 13.3 Future Works in Eastern Siberia. , References.
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  • 2
    Keywords: Forests and forestry ; Terrestial Ecology ; Ecology ; Geoecology. ; Environmental geology. ; Climate change. ; Hydrology. ; Meteorology. ; Forestry.
    Description / Table of Contents: Introduction -- Atmospheric water cycle -- Water cycles in forests -- Carbon cycles in forests -- Methane and biogenic volatile organic compound emissions in eastern Siberia -- Stable isotopes of water in Permafrost ecosystem -- Water-carbon cycle in dendrochronology -- Permafrost-forest dynamic -- River Discharge -- Remote sensing of vegetation -- Remote sensing of terrestrial water -- Water-carbon cycle modeling -- Concluding Remarks
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (X, 309 p. 97 illus., 62 illus. in color)
    Edition: 1st ed. 2019
    ISBN: 9789811363177
    Series Statement: Ecological Studies, Analysis and Synthesis 236
    Language: English
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  • 3
    Publication Date: 2021-07-19
    Description: In this study latent heat flux (λE) measurements made at 65 boreal and arctic eddy-covariance (EC) sites were analyses by using the Penman–Monteith equation. Sites were stratified into nine different ecosystem types: harvested and burnt forest areas, pine forests, spruce or fir forests, Douglas-fir forests, broadleaf deciduous forests, larch forests, wetlands, tundra and natural grasslands. The Penman–Monteith equation was calibrated with variable surface resistances against half-hourly eddy-covariance data and clear differences between ecosystem types were observed. Based on the modeled behavior of surface and aerodynamic resistances, surface resistance tightly control λE in most mature forests, while it had less importance in ecosystems having shorter vegetation like young or recently harvested forests, grasslands, wetlands and tundra. The parameters of the Penman–Monteith equation were clearly different for winter and summer conditions, indicating that phenological effects on surface resistance are important. We also compared the simulated λE of different ecosystem types under meteorological conditions at one site. Values of λE varied between 15% and 38% of the net radiation in the simulations with mean ecosystem parameters. In general, the simulations suggest that λE is higher from forested ecosystems than from grasslands, wetlands or tundra-type ecosystems. Forests showed usually a tighter stomatal control of λE as indicated by a pronounced sensitivity of surface resistance to atmospheric vapor pressure deficit. Nevertheless, the surface resistance of forests was lower than for open vegetation types including wetlands. Tundra and wetlands had higher surface resistances, which were less sensitive to vapor pressure deficits. The results indicate that the variation in surface resistance within and between different vegetation types might play a significant role in energy exchange between terrestrial ecosystems and atmosphere. These results suggest the need to take into account vegetation type and phenology in energy exchange modeling.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 4
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Science Ltd
    Global change biology 9 (2003), S. 0 
    ISSN: 1365-2486
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Notes: To understand the effects of climate change on the growing season of plants in Japan, we conducted trend analysis of phenological phases and examined the relationship between phenology and air temperatures. We used phenological data for Ginkgo biloba L., collected from 1953 to 2000. We defined the beginning and the end of the growing season (BGS and EGS) as the dates of budding and leaf fall, respectively. Changes in the air temperature in the 45 days before the date of BGS affected annual variation in BGS. The annual variation in air temperature over the 85 days before EGS affected the date of EGS. The average annual air temperature in Japan has increased by 1.3°C over the last four decades (1961–2000), and this increase has caused changes in ginkgo phenology. In the last five decades (1953–2000), BGS has occurred approximately 4 days earlier than previously, and EGS has occurred about 8 days later. Consequently, since 1953 the length of the growing season (LGS) has been extended by 12 days. Since around 1970, LGS and air temperatures have shown increasing trends. Although many researchers have stated that phenological events are not affected by the air temperature in the fall, we found high correlations not only between budding dates and air temperatures in spring but also between leaf-fall dates and air temperatures in autumn. If the mean annual air temperature increases by 1°C, LGS could be extended by 10 days. We also examined the spatial distribution of the rate of LGS extension, but we did not find an obvious relationship between LGS extension and latitude.
    Type of Medium: Electronic Resource
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  • 5
    Publication Date: 2014-05-01
    Description: We have developed a new algorithm that improves near-field tsunami forecasting based on offshore tsunami data soon after an earthquake by incorporating real-time onshore Global Navigation Satellite System (GNSS) data. In our algorithm, called tFISH/RAPiD, the initial sea-surface height distribution estimated from rapidly acquired GNSS data provides robust finite source size information that is incorporated into an offshore tsunami data inversion for reliable tsunami predictions along the near-field coast. Our algorithm can be applicable to arbitrary types of large tsunamigenic earthquakes when the static displacements are substantial enough to be detected at onshore GNSS stations. We retrospectively applied our algorithm to the 2011 M w 9.0 Tohoku earthquake and demonstrated its ability to provide information about disastrous tsunamis approaching wide areas along the near-field coast. Furthermore, arrival times and wave heights of large-amplitude, short-period tsunamis affecting specific near-field coasts can be predicted at least 5 minutes before the actual tsunami arrivals.
    Print ISSN: 0094-8276
    Electronic ISSN: 1944-8007
    Topics: Geosciences , Physics
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  • 6
    Publication Date: 2015-10-10
    Description: The subduction rate of an oceanic plate may accelerate after large earthquakes rupture the interplate coupling between the oceanic and overriding continental plate. To better understand postseismic deformation processes in an incoming oceanic plate, we directly measured the displacement rate of the Pacific Plate near the Japan Trench after the 2011 Tohoku-Oki earthquake using a GPS/Acoustic technique over a period of two years (September 2012 to September 2014). The displacement rate was measured to be 18.0±4.5 cm yr −1 (N302.0°E) relative to the North American Plate, which is almost twice as fast as the predicted interseismic plate motion. Because the sum of steady plate motion and viscoelastic response to the Tohoku-Oki earthquake roughly accounts for the observed displacement rate, we conclude that viscoelastic relaxation is the primary mechanism responsible for postseismic deformation of the Pacific Plate and that significant subduction acceleration did not occur, at least not during the observation period.
    Print ISSN: 0094-8276
    Electronic ISSN: 1944-8007
    Topics: Geosciences , Physics
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