Abstract
In this study, catchments are considered as complex systems; and information-theoretic measures are used to capture temporal streamflow characteristics. Emergence and self-organization are used to quantify information production and order in streamflow time series respectively. The measure, complexity is used to quantify the balance between emergence and self-organization in streamflow variability. The complexity measure is found to be effective in distinguishing streamflow variability for high and low snow-dominated catchments. The state of persistence – reflecting the memory of streamflow time series, is shown to be related with complexity of streamflow. Moreover, it is observed that conventional causal detection methods are constrained by state of persistence and more robust methods are needed in hydrological applications considering persistence.
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