North American precipitation isotope (δ18O) zones revealed in time series modeling across Canada and northern United States. Issue 2 (27th February 2015)

• Record Type: Journal Article
• Authors: Delavau, C.; Chun, K. P.; Stadnyk, T.; Birks, S. J.; Welker, J. M.
• Is Part Of: Water resources research. Volume 51:Issue 2(2015:Feb.)
• Page Start: 1284
• Page End: 1299
• Publication Date: 2015-02-27
• Subjects: spatial distribution -- empirical modelling -- stable isotope geochemistry -- oxygen‐18 -- meteoric precipitation -- uncertainty; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2014WR015687
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Delineating spatial patterns of precipitation isotopes ("isoscapes") is becoming increasingly important to understand the processes governing the modern water isotope cycle and their application to migration forensics, climate proxy interpretation, and ecohydrology of terrestrial systems. However, the extent to which these patterns can be empirically predicted across Canada and the northern United States has not been fully articulated, in part due to a lack of time series precipitation isotope data for major regions of North America. In this study, we use multiple linear regressions of CNIP, GNIP, and USNIP observations alongside climatological variables, teleconnection indices, and geographic indicators to create empirical models that predict the δ 18 O of monthly precipitation (δ 18 Oppt ) across Canada and the northern United States. Five regionalization approaches are used to separate the study domain into isotope zones to explore the effect of spatial grouping on model performance. Stepwise regression‐derived parameterizations quantified by permutation testing indicate the significance of precipitable water content and latitude as predictor variables. Within the Canadian Arctic and eastern portion of the study domain, models from all regionalizations capture the interannual and intraannual variability of δ 18 Oppt . The Pacific coast and northwestern portions of the study domain show less agreement between models and poorer model performance, resulting in higher uncertainty in simulations throughout these regions. Long‐term annual average δ 18 Oppt isoscapes are generated, highlighting the uncertainty in the regionalization approach as it compounds over time. Additionally, monthly time series simulations are presented at various locations, and model structure uncertainty and 90% bootstrapped prediction bounds are detailed for these predictions. Key Points: Empirical models are developed to simulate 18O of monthly precipitation Precipitable water content describes the most variance in precipitation 18O Uncertainty in modeling monthly and long‐term precipitation 18O is assessed