Linking Hydrological Processes With Water Resource Insights Through Synthesis of Big Data and Physics-Based Modeling
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Authors
Boardman, Elijah Nathaniel
Issue Date
2025
Type
Dissertation
Language
en_US
Keywords
Climate change , Forest fire , Model calibration , Mountain hydrology , Snow science , Stream network
Alternative Title
Abstract
Mountain hydrologists provide guidance on questions of water resource use and protection that affect global environments, billions of dollars in economic activity, and the entire way of life of societies dependent on upland water sources. However, the tools for making these assessments are uncertain, limited by sparse data, and may provide conflicting interpretations. The two broad philosophies of scientific inquiry, inductive and deductive reasoning, appear in hydrology as empirical observation-centric approaches or theoretical simulation-based approaches. Both of these approaches have particular strengths: direct observations can reveal the uniqueness of place that is essential to many hydrological processes, while generalized simulations can reveal emergent behaviors and provide clear cause-and-effect attribution. In the research presented here, I show how advancements in computational power and “big data” can help unite inductive and deductive hydrological philosophies by using large datasets to make more generalizable inferences and using more comprehensive simulations to make more incisive predictions. These chapters provide a tour of current focus areas in mountain hydrology, including water supply forecasting, forest disturbance and management, climate change resilience, surface-groundwater interactions, and meadow restoration. Through these diverse study topics spanning the Sierra Nevada and Rocky Mountains, I demonstrate the synthesis of deductive and inductive approaches to condense massive datasets and complex model results into actionable insights that can inform best practices for the protection and utilization of valuable mountain water resources.