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Quantifying the complexity in mapping energy inputs and hydrologic state variables into land‐surface fluxes
Center for Nonlinear and Complex Systems, Duke University
School of the Environment, Duke University
Department of Environmental Sciences, University of Virginia
School of Industrial and Systems Engineering, Georgia Institute of Technology
School of the Environment, Duke University
Humanities and General Sciences, National Taiwan University of Science and Technology
School of the Environment, Duke University
This study explores the complexity (or disorder) in mapping energy (Rn ) forcing to land surface fluxes of sensible heat (Hs ), water vapor (LE), and carbon dioxide (or net ecosystem exchange, NEE) for different soil water states (θ). Specifically, we ask, does the vegetation act to increase or dissipate statistical entropy injected from Rn ? We address this question using novel scalar complexity measures applied to a long‐term time series record of Rn , θ, Hs , LE, and NEE collected over a uniform pine forest. This analysis is the first to demonstrate that vegetation dissipates scalar flux entropy injected through Rn . We also find that the entropy or disorder in scalar fluxes increases with increasing Rn and that the complexity in mapping Rn to scalar fluxes is reduced with increasing θ.
Received 1 August 2000; accepted 1 August 2000; .
Citation: (2001), Quantifying the complexity in mapping energy inputs and hydrologic state variables into land‐surface fluxes, Geophys. Res. Lett., 28(17), 3305–3307.
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