The Landscape Evolution Observatory: A large-scale controllable infrastructure to study coupled Earth-surface processes

Autor: Katerina Dontsova, Jon D. Pelletier, Guo Yue Niu, David D. Breshears, Stephen B. DeLong, Nate Abramson, Peter Troch, Brendan P. Murphy, Whitney Henderson, Xubin Zeng, David Millar, Joost van Haren, Joaquin Ruiz, Scott R. Saleska, Ty P. A. Ferré, Mitch Pavao-Zuckerman, Michael Sibayan, Marcel G. Schaap, Jon Chorover, Edward A. Hunt, Craig Rasmussen, Javier E. Espeleta, Markus Tuller, John Adams, Paul D. Brooks, Travis E. Huxman, Luke A. Pangle, William E. Dietrich, Matej Durcik, Régis Ferrière, Greg A. Barron-Gafford
Rok vydání: 2015
Předmět:
Zdroj: Geomorphology. 244:190-203
ISSN: 0169-555X
DOI: 10.1016/j.geomorph.2015.01.020
Popis: Zero-order drainage basins, and their constituent hillslopes, are the fundamental geomorphic unit comprising much of Earth's uplands. The convergent topography of these landscapes generates spatially variable substrate and moisture content, facilitating biological diversity and influencing how the landscape filters precipitation and sequesters atmospheric carbon dioxide. In light of these significant ecosystem services, refining our understanding of how these functions are affected by landscape evolution, weather variability, and long-term climate change is imperative. In this paper we introduce the Landscape Evolution Observatory (LEO): a large-scale controllable infrastructure consisting of three replicated artificial landscapes (each 330 m 2 surface area) within the climate-controlled Biosphere 2 facility in Arizona, USA. At LEO, experimental manipulation of rainfall, air temperature, relative humidity, and wind speed are possible at unprecedented scale. The Landscape Evolution Observatory was designed as a community resource to advance understanding of how topography, physical and chemical properties of soil, and biological communities coevolve, and how this coevolution affects water, carbon, and energy cycles at multiple spatial scales. With well-defined boundary conditions and an extensive network of sensors and samplers, LEO enables an iterative scientific approach that includes numerical model development and virtual experimentation, physical experimentation, data analysis, and model refinement. We plan to engage the broader scientific community through public dissemination of data from LEO, collaborative experimental design, and community-based model development.
Databáze: OpenAIRE