Predictive models for strontium isotope distributions in bedrock, water and environmental materials for regional provenance studies

Strontium isotope ratio (87Sr/86Sr) has a strong potential to complement atmospherically-derived traditional stable isotopes in geochemical provenance studies because strontium (Sr) in Earth surface reservoirs is sourced from local bedrock. As such, 87Sr/ 86Sr variations are discrete and differ drastically from the large scale smoothed variations of atmospherically-derived stable isotopes. Among the most successful recent applications, 87Sr/86Sr has been used to interpret provenance of individuals in archeology, to identify the origin of dust aerosols, to reconstruct cation source and mobility in rivers, and to reconstruct animal or material movement pathways. However, extending the applications of 87Sr/86Sr for provenance to larger spatial scales is currently hampered by the absence of methods to predict the 87Sr/86Sr of Sr sources at the regional scale. In this dissertation, a flexible geostatistical framework is established to predict 87Sr/86Sr distributions in bedrock, river water and soil water at regional scale. This approach leverages publically-available geospatial data on rock geochemistry, surficial and bedrock geology, climate, hydrology, and aerosols to model the input and propagation of Sr from multiple geological sources through hydrosystems and ecosystems. In a first step, we develop predictive models for 87Sr/ 86Sr in bedrock as a function of variations in rock age and rock type. In a second step, we model the Sr release from different rock units, its transport as dissolved Sr or in aerosols, and its accumulation and mixing in ecosystems. The model was tested for the contiguous USA and circum-Caribbean region and the model showed promising results but the predictive power remained too low for routine provenance interpretations. In a final step, we develop a flexible geochemical framework that explicitly accounts for prediction uncertainty and local variability of 87Sr/86Sr and includes a Sr-specific process-based chemical weathering model. This improved model version is applied to predict 87Sr/86Sr in bedrock and rivers over Alaska and explain 82% of 87Sr/86Sr variance in Alaska Rivers. Integrated into a multi-isotopes framework, 87Sr/86Sr could dramatically improve the spatial resolution of provenance assignments. Predictive 87Sr/86Sr models are also a powerful standalone tool to visualize, identify and model mechanistic processes influencing local to global 87Sr/ 86Sr in Earth surface reservoirs.