Enhancing Agricultural Sustainability Under Climate Change: A Multi-Scale Framework Integrating Climate Extremes, Resource Efficiency, and Data-Driven Modeling

Author

• Shahryar Fazli, Chapman UniversityFollow

Date of Award

Fall 12-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computational and Data Sciences

First Advisor

Hesham El-Askary

Second Advisor

Joshua B. Fisher

Third Advisor

Charles Ichoku

Fourth Advisor

Wenzhao Li

Abstract

Agricultural systems are increasingly challenged by climate variability, where shifting temperature regimes, hydrological variability, and the rising frequency of compound and cascading extremes threaten global food security and resource sustainability. Addressing these challenges requires integrated frameworks that bridge biophysical monitoring, predictive modeling, and adaptive decision-making. This dissertation develops a data-driven, multi-scale framework to quantify and enhance agricultural resilience by integrating remote sensing, climate analytics, and machine learning across the United States, with a focus on California and the Western U.S.

First, hyperspectral and thermal remote sensing data from EMIT and OpenET are integrated to characterize crop nitrogen–water interactions and assess nutrient and water use efficiency. Second, climate-driven projections of water use efficiency are combined with machine learning and CMIP6 climate scenarios to develop adaptive crop-switching strategies under alternative water-saving, economic, and food-security objectives. Third, a comprehensive climate-extremes framework is developed to quantify the frequency, intensity, spatial distribution, and agricultural exposure associated with single, compound, and cascading extremes over multi-decadal periods. Finally, an advanced spatiotemporal machine learning architecture is introduced for crop-yield forecasting that explicitly accounts for temporal variability and changing climate conditions.

The results reveal strong crop-dependent spatial coupling between canopy nitrogen and evapotranspiration, demonstrating the potential of integrated hyperspectral and water-use observations for identifying variations in agricultural resource efficiency. Climate-adaptive crop-switching simulations show that strategic changes in crop allocation can substantially reduce agricultural water demand while maintaining food production and improving economic returns under future climate scenarios. The climate-extremes analysis identifies pronounced spatial and temporal shifts in agricultural exposure, with heat- and drought-related single, compound, and cascading events emerging as increasingly important hazards and their coincidence with sensitive crop phenological stages amplifying potential impacts. Finally, the spatiotemporal yield-forecasting framework improves predictive robustness across changing climatic conditions, demonstrating the importance of explicitly accounting for temporal variability in agricultural prediction. Together, these findings show that agricultural resilience depends not on any single climate or management factor, but on interactions among climate hazards, crop biophysical responses, resource use, and adaptive management.

Collectively, this work advances the understanding of agricultural systems as interconnected climate–biophysical–management networks and provides actionable insights for sustainable agriculture. The findings directly contribute to global sustainability efforts, particularly Sustainable Development Goals related to zero hunger (SDG 2), clean water management (SDG 6), responsible resource use (SDG 12), and climate action (SDG 13). By integrating observation, prediction, and adaptation, this dissertation establishes a scalable framework for supporting resilient, data-driven agricultural systems under a changing climate.

Creative Commons License

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

Recommended Citation

S. Fazli, "Enhancing agricultural sustainability under climate change: A multi-scale framework integrating climate extremes, resource efficiency, and data-driven modeling," Ph.D. dissertation, Chapman University, Orange, CA, 2026. https://doi.org/10.36837/chapman.000767