Highly sampled measurements in a controlled atmosphere at the Biosphere 2 Landscape Evolution Observatory
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Published:2020-09-15
Issue:1
Volume:7
Page:
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ISSN:2052-4463
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Container-title:Scientific Data
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language:en
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Short-container-title:Sci Data
Author:
Arevalo JorgeORCID, Zeng XubinORCID, Durcik Matej, Sibayan Michael, Pangle Luke, Abramson Nate, Bugaj Aaron, Ng Wei-Ren, Kim Minseok, Barron-Gafford GregORCID, van Haren Joost, Niu Guo-YueORCID, Adams John, Ruiz Joaquin, Troch Peter A.
Abstract
AbstractLand-atmosphere interactions at different temporal and spatial scales are important for our understanding of the Earth system and its modeling. The Landscape Evolution Observatory (LEO) at Biosphere 2, managed by the University of Arizona, hosts three nearly identical artificial bare-soil hillslopes with dimensions of 11 × 30 m2 (1 m depth) in a controlled and highly monitored environment within three large greenhouses. These facilities provide a unique opportunity to explore these interactions. The dataset presented here is a subset of the measurements in each LEO’s hillslopes, from 1 July 2015 to 30 June 2019 every 15 minutes, consisting of temperature, water content and heat flux of the soil (at 5 cm depth) for 12 co-located points; temperature, relative humidity and wind speed above ground at 5 locations and 5 different heights ranging from 0.25 m to 9–10 m; 3D wind at 1 location; the four components of radiation at 2 locations; spatially aggregated precipitation rates, total subsurface discharge, and relative water storage; and the measurements from a weather station outside the greenhouses.
Funder
Philecology Foundation: charitable donation for LEO construction. Agnese Nelms Haury Program in Environment and Social Justice.
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Reference35 articles.
1. Wood, E. et al. Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water. Water Resour. Res. 47(5), (2011). 2. Prentice, I., Liang, X., Medlyn, B. & Wang, Y. Reliable, robust and realistic: the three R’s of next-generation land-surface modelling. Atmos. Chem. Phys. 15, 5987–6005 (2015). 3. Bierkens, M. et al. Hyper‐resolution global hydrological modelling: what is next? “Everywhere and locally relevant”. Hydrol. Process. 29, 310–320 (2015). 4. Prodhomme, C., Doblas-Reyes, F., Bellprat, O. & Dutra, E. Impact of land-surface initialization on sub-seasonal to seasonal forecasts over Europe. Clim. Dynam. 47, 919–935 (2016). 5. Jimenez, P., de Arellano, J., Navarro, J. & Gonzalez-Rouco, J. Understanding land–atmosphere interactions across a range of spatial and temporal scales. B. Am. Meteorol. Soc. 95, ES14–ES17 (2014).
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