Detecting Regimes of Critical Zone Processes, Drivers and Predictability With a Data-Driven Framework

In highly productive agricultural landscapes, human impacts combine with naturally variable climate conditions and geologic legacies to fundamentally alter critical zone (CZ) processes. For example, artificial drainage and fertilizer applications impact riverine transport of water and nutrients, and abrupt transitions in vegetation states impact land-atmosphere fluxes of water and energy. Subsystem components may switch from being nearly independent to tightly synchronized, and drivers may elicit non-linear or threshold responses. These shifts have implications for predictive understanding, but are challenging to identify and quantify. We present a flexible, data-driven framework that identifies temporal regimes in CZ behavior, associated with particular environmental drivers and modes of variability. We integrate unsupervised clustering, dimensionality reduction, and information-theoretic metrics to isolate temporal regimes and assess changes in drivers and predictability across several case studies. Specifically, we analyze high-frequency time-series data sets of root-soil gases, land-atmosphere fluxes, and river chemistry in intensively managed and more natural CZ study sites. Clusters and variability in these multivariate systems relate to rapid seasonal transitions in agricultural relative to prairie sites, and impacts of fertilizer timing on solute responses to events. Environmental drivers have variable explanatory power over system states that align with growing season, flows, or management. This study objectively detects dynamic transitions in CZ systems, and is more broadly applicable to any Earth system time-series data or model evaluation to detect behavioral regimes and shifts in predictability of complex systems.