Critical Zone network cluster research: Using Big Data approaches to assess ecohydrological resilience across scales

While observatory-based critical zone (CZ) research produces important findings on catchment-scale processes, the global scale of disturbances in the Anthropocene transcends the bounds of a single site or funding cycle, posing a challenge for traditional investigative approaches. This spatial and temporal mismatch significantly limits the descriptive and predictive power of results from individual catchment-scale studies in the context of identifying patterns at regional- to continental-scale environmental change. To advance network-scale syntheses and integrate across scales, this CZ network cluster project newly funded by NSF will apply an iterative “pattern to process” and “process to pattern” approach to investigate how CZ structure controls water, carbon, nutrients, and response to disturbances overlapping in the context of multi-dimensional resilience. The overarching hypothesis is that CZ structure (i.e., configuration of biological, chemical, and physical characteristics) controls the timing, direction, and intensity of linkages among multiple responses and that these linkages regulate ecosystem resilience and resistance to climate and land cover disturbance. In order to motivate the science community to join these research and educational efforts, in this presentation we will i) provide an overview of the project goals in research and education and ii) present examples of how data science and CZ science can be combined to enable multi-dimensional resilience investigation across scales.