Goodwell et al., 2018

Paper/Book

Dynamic process connectivity explains ecohydrologic responses to rainfall pulses and drought

Goodwell, A.E., Kumar, P., Fellows, A.W., and Flerchinger, G.N. (2018)
PNAS  

Abstract

Ecohydrologic fluxes within atmosphere, vegetation, and soil systems exhibit a joint variability that arises from forcing and feedback interactions. These interactions cause fluctuations to propagate between variables at many time scales. In an ecosystem, this connectivity dictates responses to climate change, land-cover change, and weather events and must be characterized to understand resilience and sensitivity. We use an information theory-based approach to quantify connectivity in the form of information flow associated with the propagation of fluctuations between variables. We apply this approach to study ecosystems that experience changes in dry-season moisture availability due to rainfall and drought conditions. We use data from two transects with flux towers located along elevation gradients and quantify redundant, synergistic, and unique flow of information between lagged sources and targets to characterize joint asynchronous time dependencies. At the Reynolds Creek Critical Zone Observatory in Idaho, a dry-season rainfall pulse leads to increased connectivity from soil and atmospheric variables to heat and carbon fluxes. At the Southern Sierra Critical Zone Observatory in California, separate sets of dominant drivers characterize two sites at which fluxes exhibit different drought responses. For both cases, our information flow-based connectivity characterizes dominant drivers and joint variability before, during, and after disturbances. This approach to gauge the responsiveness of ecosystem fluxes under multiple sources of variability furthers our understanding of complex ecohydrologic systems.

 

 

Citation

Goodwell, A.E., Kumar, P., Fellows, A.W., and Flerchinger, G.N. (2018): Dynamic process connectivity explains ecohydrologic responses to rainfall pulses and drought. PNAS. DOI: doi.org/10.1073/pnas.1800236115

This Paper/Book acknowledges NSF CZO grant support.