Comparison of Radiocesium in Alaskan Tundra Plants, 1961 and 1971

1975 ◽  
Vol 7 (3) ◽  
pp. 285
Author(s):  
W. H. Rickard ◽  
J. D. Hedlund ◽  
H. A. Sweany
Keyword(s):  
Alaskan Tundra

Effects of CO2 enrichment and nutrition on growth, photosynthesis, and nutrient concentration of Alaskan tundra plant species

1986 ◽  
Vol 64 (12) ◽  
pp. 2993-2998 ◽  
Author(s):  
Steven F. Oberbauer ◽  
Nasser Sionit ◽  
Steven J. Hastings ◽  
Walter C. Oechel
Keyword(s):  
Plant Biomass ◽  
Co2 Enrichment ◽  
Nutrient Content ◽  
Interactive Effects ◽  
Photon Flux ◽  
Nutrient Concentrations ◽  
Total Biomass ◽  
Ledum Palustre ◽  
Carbon Dioxide Concentrations ◽  
Alaskan Tundra

Three Alaskan tundra species, Carex bigelowii Torr., Betula nana L., and Ledum palustre L., were grown in controlled-environment chambers at two nutrition levels with two concentrations of atmospheric CO2 to assess the interactive effects of these factors on growth, photosynthesis, and tissue nutrient content. Carbon dioxide concentrations were maintained at 350 and 675 μL L−1 under photosynthetic photon flux densities of 450 μmol m−2 s−1 and temperatures of 20:15 °C (light:dark). Nutrient treatments were obtained by watering daily with 1/60- or 1/8- strength Hoagland's solution. Leaf, root, and total biomass were strongly enhanced by nutrient enrichment regardless of the CO2 concentration. In contrast, enriched atmospheric CO2 did not significantly affect plant biomass and there was no interaction between nutrition and CO2 concentration during growth. Leaf photosynthesis was increased by better nutrition in two species but was unchanged by CO2 enrichment during growth in all three species. The effects of nutrient addition and CO2 enrichment on tissue nutrient concentrations were complex and differed among the three species. The data suggest that CO2 enrichment with or without nutrient limitation has little effect on the biomass production of these three tundra species.

scholarly journals Climate Drivers Linked to Changing Seasonality of Alaska Coastal Tundra Vegetation Productivity

2015 ◽  
Vol 19 (19) ◽  
pp. 1-29 ◽  
Author(s):  
Peter A. Bieniek ◽  
Uma S. Bhatt ◽  
Donald A. Walker ◽  
Martha K. Raynolds ◽  
Josefino C. Comiso ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sea Breeze ◽  
Tundra Vegetation ◽  
Land Surface Temperatures ◽  
Alaskan Tundra ◽  
Northern Alaska

Abstract The mechanisms driving trends and variability of the normalized difference vegetation index (NDVI) for tundra in Alaska along the Beaufort, east Chukchi, and east Bering Seas for 1982–2013 are evaluated in the context of remote sensing, reanalysis, and meteorological station data as well as regional modeling. Over the entire season the tundra vegetation continues to green; however, biweekly NDVI has declined during the early part of the growing season in all of the Alaskan tundra domains. These springtime declines coincide with increased snow depth in spring documented in northern Alaska. The tundra region generally has warmed over the summer but intraseasonal analysis shows a decline in midsummer land surface temperatures. The midsummer cooling is consistent with recent large-scale circulation changes characterized by lower sea level pressures, which favor increased cloud cover. In northern Alaska, the sea-breeze circulation is strengthened with an increase in atmospheric moisture/cloudiness inland when the land surface is warmed in a regional model, suggesting the potential for increased vegetation to feedback onto the atmospheric circulation that could reduce midsummer temperatures. This study shows that both large- and local-scale climate drivers likely play a role in the observed seasonality of NDVI trends.

Exploring environmental factors driving wildfire occurrences in Alaskan tundra

2021 ◽  
Author(s):  
Jiaying He ◽  
Tatiana Loboda ◽  
Nancy French ◽  
Dong Chen
Keyword(s):  
Environmental Factors ◽  
Weather Conditions ◽  
Empirical Modeling ◽  
The Arctic ◽  
Fire Weather ◽  
Fuel Type ◽  
Ignition Probability ◽  
Alaskan Tundra ◽  
Fire Ignition ◽  
Cg Lightning

<p>Tundra fires are common across the pan-Arctic region, particularly in Alaska. Fires lead to significant impacts on terrestrial carbon balance and ecosystem functioning in the tundra. They can even affect the forage availability of herbivorous wildlife and living resources of local human communities. Also, interactions between fire and climate change can enhance the fire impacts on the Arctic ecosystems. However, the drivers and mechanisms of wildland fire occurrences in Alaskan tundra are still poorly understood. Research on modeling contemporary fire probability in the tundra is also lacking. This study focuses on exploring the critical environmental factors controlling wildfire occurrences in Alaskan tundra and modeled the fire ignition probability, accounting for ignition source, fuel types, fire weather conditions, and topography. The fractional cover maps of fuel type components developed Chapter 2 serve as input data for fuel type distribution. The probability of cloud-to-ground (CG) lightning and fire weather conditions are simulated using WRF. Topographic features are also calculated from the Digital Elevation Model (DEM) data. Additionally, fire ignition locations are extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product for Alaskan tundra from 2001 to 2019. Empirical modeling methods, including RF and logistic regression, are then utilized to model the relationships between environmental factors and wildfire occurrences in the tundra and to evaluate the roles of these factors. Our results suggested that CG lightning is the primary driver controlling fire ignitions in the tundra, while warmer and drier weather conditions also support fires. We also projected future potential of wildland fires in this tundra region with Coupled Model Intercomparison Projects Phase 6 (CMIP6) data. The results of this study highlight the important role of CG lightning in driving tundra fires and that incorporating CG lightning modeling is necessary and essential for fire monitoring and management efforts in the High Northern Latitudes (HNL).</p>

scholarly journals Water uptake of Alaskan tundra evergreens during the winter-spring transition

2016 ◽  
Vol 103 (2) ◽  
pp. 298-306 ◽  
Author(s):  
Jonathan G. Moser ◽  
Steven F. Oberbauer ◽  
Leonel da S. L. Sternberg ◽  
Patrick Z. Ellsworth ◽  
Gregory Starr ◽  
...  
Keyword(s):  
Water Uptake ◽  
Spring Transition ◽  
Alaskan Tundra

Evidence for re-distribution of137Cs in Alaskan tundra, lake, and marine sediments

1995 ◽  
Vol 160-161 ◽  
pp. 295-306 ◽  
Author(s):  
L.W. Cooper ◽  
J.M. Grebmeier ◽  
I.L. Larsen ◽  
C. Solis ◽  
C.R. Olsen
Keyword(s):  
Marine Sediments ◽  
Alaskan Tundra

Atmospheric methane sources: Alaskan tundra bogs, an alpine fen, and a subarctic boreal marsh

Tellus B ◽  
1986 ◽  
Vol 38B (1) ◽  
pp. 1-10 ◽  
Author(s):  
DANIEL I. SEBACHER ◽  
ROBERT C. HARRISS ◽  
KAREN B. BARTLETT ◽  
SHIRLEY M. SEBACHER ◽  
SHIRLEY S. GRICE
Keyword(s):  
Atmospheric Methane ◽  
Alaskan Tundra

scholarly journals Emissions of biogenic sulfur gases from Alaskan tundra

1992 ◽  
Vol 97 (D15) ◽  
pp. 16703 ◽  
Author(s):  
Mark E. Hines ◽  
Michael C. Morrison
Keyword(s):  
Alaskan Tundra

scholarly journals Estimating Random Uncertainty in Airborne Flux Measurements over Alaskan Tundra: Update on the Flux Fragment Method

2017 ◽  
Vol 34 (8) ◽  
pp. 1807-1822 ◽  
Author(s):  
Ronald Dobosy ◽  
David Sayres ◽  
Claire Healy ◽  
Edward Dumas ◽  
Mark Heuer ◽  
...  
Keyword(s):  
Error Estimate ◽  
Random Error ◽  
Surface Flux ◽  
Surface Fluxes ◽  
Process Models ◽  
Turbulence Measurement ◽  
Surface Exchange ◽  
Time Space ◽  
Alaskan Tundra ◽  
Fragment Method

AbstractAirborne turbulence measurement gives a spatial distribution of air–surface fluxes that networks of fixed surface sites typically cannot capture. Much work has improved the accuracy of such measurements and the estimation of the uncertainty peculiar to streams of turbulence data measured from the air. A particularly significant challenge and opportunity is to distinguish fluxes from different surface types, especially those occurring in patches smaller than the necessary averaging length. The flux fragment method (FFM), a conditional-sampling variant of eddy covariance in the space–time domain, was presented in 2008. It was shown capable of segregating the mean flux density (CO2, H2O, sensible heat) in maize from that in soybeans over the patchwork farmlands of Illinois. This was, however, an ideal surface for the method, and the random-error estimate used a relatively rudimentary bootstrap resampling. The present paper describes an upgraded random-error estimate that accounts for the serial correlation of the time/space series and the heterogeneity of the signal. Results are presented from the Alaskan tundra. Though recognized as important, systematic error estimates are not covered in this paper. Some discussion is offered on the relation of the FFM to other approaches similarly motivated, particularly those using wavelets. Successful measurement of the variation of air–surface exchange over heterogeneous surfaces has value for developing and improving process models relating surface flux to remotely sensible quantities, such as the vegetative land-cover type and its condition.

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