Skip navigation
Please use this identifier to cite or link to this item:
Title: Land Surface Models in Hydrologic Monitoring Systems: Addressing the Sources of Uncertainty
Authors: Chaney, Nathaniel Wilkins
Advisors: Wood, Eric F.
Contributors: Civil and Environmental Engineering Department
Keywords: Big Data
High Performance Computing
Land Surface Models
Model Uncertainty
Subjects: Hydrologic sciences
Issue Date: 2015
Publisher: Princeton, NJ : Princeton University
Abstract: Hydrologic extremes can have devastating consequences on ecosystems, infrastructure, agriculture, and economies. Providing reliable real-time information to stakeholders and decision makers is valuable to mitigate the impact of these events; land surface models, in conjunction with numerical weather forecasting and satellite remote sensing, present a unique opportunity to make this goal a reality. However, uncertainties in the parameterizations, input data, and parameter values in land surface models limit these systems' reliability and applications. The objective of this dissertation is to quantify and reduce a subset of these uncertainties. Chapter 2 assesses the skill of current macroscale land surface models to simulate the observed spatial heterogeneity of land surface processes. Chapter 3 builds on this assessment to develop HydroBloks-a state-of-the-art land surface model that explicitly represents field-scale land surface processes while ensuring computational efficiency. To reduce the uncertainty in model input data, Chapter 4 introduces an algorithm that merges ground truth observations into gridded meteorological forcing datasets. Chapter 5 addresses the lack of field-scale soil data over continental extents by using high performance computing, machine learning, and a century's worth of legacy soil data to develop the Probabilistic Remapping of SSURGO (POLARIS) dataset. Chapter 6 assesses the role of model parameter equifinality in land surface models with an emphasis on understanding the limitations of constraining models with low-quality runoff observations. Finally, Chapter 7 uses the FLUXNET network of eddy covariance sites to improve parameter estimates in the Noah land surface model. In the context of hydrologic monitoring, the results from this thesis show a path towards providing hydrologic information at field scales over continental extents. This work also illustrates how this increase in model complexity requires the implementation of robust ensemble frameworks and novel parameter estimation techniques to account for the unavoidable increase in model parameter and input data uncertainty.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Civil and Environmental Engineering

Files in This Item:
File Description SizeFormat 
Chaney_princeton_0181D_11290.pdf44.47 MBAdobe PDFView/Download

Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.