UC Santa Barbara

Climate change and its associated environmental impacts pose immense challenges that require innovative approaches to address. This dissertation presents three distinct studies that showcase the application of advanced modeling and machine learning methods to investigate critical issues at the intersection of changing human and natural systems.In Chapter 1, I employ a novel modeling framework to analyze the complex relationship between vegetation dynamics and hydroclimate variability across East Africa. Empirical dynamic modeling is a data-driven approach for studying state-dependent dynamics and interactions within complex systems, enabling the identification of key driving variables and the prediction of future system behaviors. Adopting this method, the study provides insights into how the stability and vulnerability of ecosystems vary with environmental conditions, land cover type, and seasonality. In Chapter 2, I explore how various factors contribute to human displacement, focusing on the environmental drivers and mechanisms of migration in Somalia. Gravity models are a class of spatial interaction models that estimate the flow or movement between locations based on the attractiveness of the destinations and the impedance between the origin and destination. I use these models to examine the connections between climate, socio-economic, and political factors influencing population movements. Notably, I find that livelihood is an important differentiating factor in determining whether the climate strongly impacts individuals' migration patterns. In Chapter 3, I implement advanced natural language processing techniques to develop an automated system for classifying global multi-hazard disaster events from humanitarian news articles and reports. Large language models are a form of artificial intelligence and deep learning that can process, understand, and generate human language by learning from vast amounts of textual data, enabling them to perform a wide range of natural language processing tasks. By employing these models, the study demonstrates the potential of emerging technologies in improving the efficiency of disaster information retrieval and response. With a geographical framework that unifies perspectives from environmental, social, and computer science, these chapters collectively contribute to developing data-driven solutions for understanding environmental stressors.