Ecohydrological systems may be characterized as nonlinear, complex, open dissipative systems. Such systems consist of many coupled processes, and the couplings change depending on the system state or scale in space and time at which the system is analyzed. The arrangement of couplings in a complex system may be represented as a network of information flow and feedback between variables that measure system processes. The occurrence of feedback on such a network provides sufficient conditions for self-organized and nonlinear behaviors to emerge. We adapt an information-theoretic statistical method called transfer entropy for the purposes of robustly measuring the directionality, relative strength, statistical significance, and time scale of information flow between pairs of ecohydrological variables using time series data. A process network may be delineated where variables are cast as nodes and information flows as weighted directional links between them. The process network captures key couplings and time scales and represents the state of the complex system as a whole, including functional groups of variables (subsystems) and synchronization resulting from feedbacks. It is therefore able to identify interactions which are not detectable using methods which examine the system using one relationship at a time. We assemble an information flow process network using July 2003 FLUXNET data for a Midwestern corn-soybean ecohydrological system in a healthy, peak growing season state and compare the results with those using July 2005 data for the same site during a severe drought. We find that the process network during drought is substantially decoupled, and regional-scale information feedback is reduced during the drought. We conclude that the proposed process network methodology is able to identify the differences between two states of an ecohydrological system on the basis of variations in the pattern of feedback coupling on the network.
ASJC Scopus subject areas
- Water Science and Technology