Machine Learning and Geostatistical Approaches for Discovery of Weather and Climate Events Related to El Niño Phenomena

Author

Sachi Perera, Chapman UniversityFollow

Date of Award

Spring 5-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computational and Data Sciences

First Advisor

Hesham El-Askary

Second Advisor

Mohamed Allali

Third Advisor

Cyril Rakovski

Fourth Advisor

Erik Linstead

Fifth Advisor

Joshua B. Fisher

Abstract

El Nino and La Nina are worldwide environmental phenomena brought about by repetitive changes in the water temperature of the Pacific Ocean. Even though the El-Nino impact focuses on a smaller area in the Pacific Ocean near the Equator, these developments have global repercussions, where temperature and precipitation are influenced across the globe, causing droughts and floods simultaneously. In this dissertation, we first derived a drought vulnerability index for the Nile basin, identifying regions with high and low drought risk under ENSO conditions. Next, we evaluated the coherence and periodicity of the ENSO signal to detect its implications on MENA Region using earth observations, machine learning, and advanced signal processing techniques. Moreover, we examined ecological and environmental crises created by global warming and unusual weather patterns caused by El Nino and marine heatwaves in nesting sea turtle habitats. Finally, expanding this study on ENSO yielded novel ways to analyze and understand the underlying processes driving unprecedented global heat waves and their association with ENSO.

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Recommended Citation

S. Perera, "Machine learning and geostatistical approaches for discovery of weather and climate events related to El Niño phenomena," Ph.D. dissertation, Chapman University, Orange, CA, 2024. https://doi.org/10.36837/chapman.000539