Top 4 alaska béo in 2023
Below are the best information and knowledge on the subject alaska béo compiled and compiled by our own team dvn:
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1. NGEE Arctic, Canopy Spectral Reflectance, Utqiagvik (Barrow), Alaska, 2014-2016 (Dataset) | DOE Data Explorer
Author: matpetfamily.com
Date Submitted: 01/02/2020 10:12 PM
Average star voting: 3 ⭐ ( 88045 reviews)
Summary: The U.S. Department of Energy’s Office of Scientific and Technical Information
Match with the search results: …. read more
2. Arrival of final 2 F-35s completes complement at Alaska base
Author: matpetfamily.com
Date Submitted: 07/10/2021 08:59 PM
Average star voting: 5 ⭐ ( 93798 reviews)
Summary: The final two F-35A Joint Strike Fighter jets have arrived at Eielson Air Force Base near Fairbanks.
Match with the search results: Alaska Nâu Đỏ Béo Ú. 17.000.000₫. Số lượng: Mua hàng….. read more
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3.
Author: www.arcus.org
Date Submitted: 05/29/2019 02:11 PM
Average star voting: 5 ⭐ ( 38728 reviews)
Summary:
Match with the search results: slider_3. slider_1. previous arrow. next arrow. Xin vui lòng nhập từ khoá để hiển thị kết quả. Trang chủDanh Mục CúnChó Alaska Malamutealaska béo ú ……. read more
4. Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems
Author: www.osti.gov
Date Submitted: 08/11/2021 04:50 PM
Average star voting: 3 ⭐ ( 36352 reviews)
Summary: Spatial heterogeneity in methane (CH4) flux requires a reliable upscaling approach to reach accurate regional CH4 budgets in the Arctic tundra. In this study, we combined the CLM-Microbe model with three footprint algorithms to scale up CH4 flux from a plot level to eddy covariance (EC) tower domains (200 m × 200 m) in the Alaska North Slope, for three sites in Utqiaġvik (US-Beo, US-Bes, and US-Brw), one in Atqasuk (US-Atq) and one in Ivotuk (US-Ivo), for a period of 2013-2015. Three footprint algorithms were the homogenous footprint (HF) that assumes even contribution of all grid cells, the gradient footprint (GF) that assumes gradually declining contribution from center grid cells to edges, and the dynamic footprint (DF) that considers the impacts of wind and heterogeneity of land surface. Simulated annual CH4 flux was highly consistent with the EC measurements at US-Beo and US-Bes. In contrast, flux was overestimated at US-Brw, US-Atq, and US-Ivo due to the higher simulated CH4 flux in early growing seasons. The simulated monthly CH4 flux was consistent to EC measurements but with different accuracies among footprint algorithms. DF algorithm performed better than HF and GF algorithms in capturing the temporal variation in daily CH4 flux in each month, while the model accuracy was similar among the three algorithms due to flat landscapes. Temporal variations in CH4 flux during 2013-2015 were predominately explained by air temperature (67-74%), followed by precipitation (22-36%). Spatial heterogeneities in vegetation fraction and elevation dominated the spatial variations in CH4 flux for all five tower domains despite relatively weak differences in simulated CH4 flux among three footprint algorithms. The CLM-Microbe model can simulate CH4 flux at both plot and landscape scales at a high temporal resolution, which should be applicable for other landscapes. Integrating land surface models with an appropriate algorithm provides a powerful tool for upscaling CH4 flux in terrestrial ecosystems.
Match with the search results: A view from the helicopter above the Barrow Environmental Observatory (BEO) field site near Barrow, Alaska. Credit: Photo by Paulo Olivas, ……. read more
