Degree
Doctor of Philosophy (PhD)
Department
Environmental Science
Document Type
Dissertation
Abstract
Effective water quality management requires a clear understanding of both the magnitude and spatial distribution of nutrient retention and export across dynamic landscapes, given their direct implications for drinking water supply, ecosystem integrity, fisheries, and recreational uses. Equally important is anticipating trade-offs from management actions, which can alter ecosystem functioning and the flow of services. Modeling tools provide a means to predict nutrient outcomes, evaluate these trade-offs, and support decision-making; however, their utility is often constrained by persistent challenges, including model uncertainty, limited applicability in data-scarce regions, and insufficient empirical foundations to inform scenario-driven decision-making. This dissertation addresses these gaps by advancing nutrient function modeling to enhance the reliability, applicability, and operational relevance of ecosystem service (ES) tools for land-use decision-making. Using the InVEST Nutrient Delivery Ratio (NDR) model, it provides both methodological and empirical contributions that improve model performance. Chapter 2 develops a standardized framework for systematic model evaluation using long-term water quality data, strengthening model reliability for watershed management. Chapter 3 expands this framework by integrating machine learning to impute historical data and transfer model parameters from well-monitored to under-monitored catchments based on hydrogeological similarity, enabling spatially explicit ES assessments where monitoring is sparse. Chapter 4 builds a longitudinal empirical foundation through a 60-year, multi-scale analysis in Puerto Rico, demonstrating that both landscape composition and spatial configuration critically influence nutrient dynamics essential for scenario-based modeling to inform decision-making. The contributions of this dissertation are transferable to other regions, offering tools to generate spatially and temporally explicit nutrient retention estimates, identify nutrient export hotspots, and guide adaptive land-use planning. This dissertation provides a foundation for refining nutrient function models, integrating scenario-based simulations, and supporting relevant strategies for water quality policy and ecosystem management.
Date
10-28-2025
Recommended Citation
Valladares Castellanos, Mariam Gabriela, "Modeling Nutrient Functions: Strengthening the Reliability and Applicability of Ecosystem Service Models for Land Use Decision-Making" (2025). LSU Doctoral Dissertations. 6916.
https://repository.lsu.edu/gradschool_dissertations/6916
Committee Chair
Douthat, Thomas
Included in
Data Science Commons, Environmental Monitoring Commons, Natural Resources Management and Policy Commons, Water Resource Management Commons