Quantifying the complexity in mapping energy inputs and hydrologic state variables into land-surface fluxes

This study explores the complexity (or disorder) in mapping energy (R-n) fo rcing to land surface fluxes of sensible heat (H,), water vapor (LE), and c arbon dioxide (or net ecosystem exchange,NEE) for different soil water stat es (0). Specifically, we ask, does the vegetation act to increase or dissip ate statistical entropy injected from R-n? We address this question using n ovel scalar complexity measures applied to a long-term time series record o f Rn, 0, H-s, LE, and NEE collected over a uniform pine forest. This analys is is the first to demonstrate that vegetation dissipates scalar flux entro py injected through R-n. We also find that the entropy or disorder in scala r fluxes increases with increasing R-n. and that the complexity in mapping R-n to scalar fluxes is reduced with increasing 0.