Date of Award

8-1-2019

Degree Name

Master of Science

Department

Geography and Environmental Resources

First Advisor

Ford, Trent

Abstract

This study examines variability in the isotopic composition of precipitation in southern Illinois, USA using a 5-year, event-based record, novel in its duration and in potential to extract important isotopic information of precipitation in a dynamic region, and the seasonality of major moisture sources. The isotopic composition of precipitation exhibited seasonal variations in δ18O and δ2H, where values are distinctively higher (lower) during summer (winter). Average values for d-excess were the highest (lowest) during autumn (summer). Seasonality is also present in events originating from the Gulf of Mexico (GOM), isolated by Langrangian methods. GOM JJA events showed more isotopically depleted precipitation than of non-GOM events due to a stronger “amount effect” signal (R2 = -0.40). To date, the amount effect has never been reported this far inland in the US. Precipitation analysis as it related to the ENSO phases exhibited statically significant differences in d-excess values. Precipitation events that occurred during a La Niña phase exhibited positive d¬-excess values, with an average value of 25.18‰. δ18O and δ2H values during El Niño phase were on average more depleted. Overall results highlight that the GOM, as a dominating moisture source, and ENSO phases can modulate the seasonal and intra-seasonal variability in the isotopic composition of precipitation in this region. The data collected, from a single location, can highlight moisture dynamics occurring on a regional scale and highlight the importance of the GOM as prevailing source of moisture for the Midwestern US. The second part of this study involved determining what were the best predictors for 18O and d-excess values. The study revealed that sea surface temperature and oxygen isotope values in the ambient vapor were the best predictors for 18O values but were the poorest predictors of d-excess values. The best predictors of d-excess were land surface characteristics such as the volumetric soil moisture, evaporation from bare soil variable, as well as SST, temperature of the air and specific humidity of the air. However, these predictors worked best with positive d-excess values that equaled or above 20‰.

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