On the History and Evidence of the Colwell Index in Quantifying Environmental Predictability, and Its Applications in Characterizing Precipitation Predictability in the Conterminous United States

The search for predictability within complex environmental noise has been one of the fundamental challenges in understanding the dynamic but self-regulating behaviors of biotic-abiotic interactions. Based on the concept of information theory, the Colwell index provides an interpretable and simple measure of predictability, and has been used extensively to facilitate the quantifications of recurrent likelihood in metric and non-metric data series. This dissertation characterizes the spatial patterns of predictability for precipitation means and extremes across the conterminous United States (US), evaluates the temporal patterns of precipitation predictability under various climate change scenarios, and provides a synthesized framework illustrating the experiences, uncertainties, and opportunities associated with the applications of the Colwell index. It is demonstrated that monthly mean precipitation and duration-based dry and wet extremes are generally highly variable in the East compared to those in the West, while the reversed spatial pattern is observed for intensity-based wetness indices, except along the Pacific Northwest coast. It is thus inferred that over much of the US landscape, variations of monthly mean precipitation are driven by variations in precipitation occurrences rather than the intensity of infrequent heavy rainfall. Furthermore, based on monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble, clear regional hotspots of future changes in precipitation predictability are identified for the conterminous US. Importantly, these hotspots coincide with regions where CMIP5 model ensembles show little change in total precipitation. As such, decision-makers are advised to not rely solely on future total precipitation as an indicator of water resources and availability. Moreover, the Colwell index represents a useful tool for supplementing traditional estimates of regularity and periodicity, and helps answer fundamental biological questions such as how important is climate and resource predictability to adaptation and evolution. However, future studies utilizing the Colwell index must provide explicit and methodologically consistent data handling decisions to justify the relevancy of the calculated predictability to the context of the study, which has not generally been made available in past studies. The work presented in this dissertation represents a solid starting point for a more meaningful classification and interpretation of environmental predictability, which in turn, could fundamentally underpin the need for scientifically sound investigations of climate change impacts.