Diagnosing spatiotemporal catchment hydrologic behavior via causal inference, information theory, and machine learning
department or school: Science, Faculty of
department or school: Earth, Ocean and Atmospheric Sciences, Department of
data provider: DSpace
degree campus: UBCV
creator: Janssen, Joseph
issued: 2025-09-11T14:19:11Z
issued: 2025
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Since all ecological- and human-centric systems rely on a steady water supply, scientists must learn the true-underlying processes that force hydrological behavior from a few thousand catchments with available data in order to transfer that knowledge to the infinite number of ungauged catchments. While hydrologists have historically preferred to utilize physics-based equations to gain and transfer knowledge, data-driven methods for hydrological inference may provide more promising approaches. Due to the unique properties of hydrological data, such as extreme spatiotemporal dependence, high measurement uncertainty, threshold-like nonlinear processes, highly skewed distributions, non stationary relationships, uncommon causal pathway types, and high order emergent interactions, traditional statistical methods fail to learn important yet still undiscovered hydrological processes. These failures arise in a variety of hydrological studies including cross-catchment spatial analyses, single-catchment temporal analyses, and single-catchment spatiotemporal analyses, thus for each of these study types, we demonstrate previous methodological shortcomings and invalid assumptions, while developing and applying new data-driven approaches relating to information theory, causal inference, and statistical learning.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/92280
note: Diagnosing Spatiotemporal CatchmentHydrologic Behavior via CausalInference, Information Theory, andMachine LearningbyJoseph JanssenA THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Geological Sciences)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)September 2025© Joseph Janssen 2025The following individuals certify that they have read, and recommend to the Faculty of Graduateand Postdoctoral Studies for acceptance, the thesis entitled:Diagnosing Spatiotemporal Catchment Hydrologic Behavior via Causal Inference, In-formation Theory, and Machine Learningsubmitted by Joseph Janssen in partial fulfillment of the requirements for the degree of Doctorof Philosophy in Geological Sciences.Examining Committee:Ali Ameli, Assistant Professor, Department of Earth, Ocean and Atmospheric Sciences, UBCSupervisorElina Robeva, Associate Professor, Department of Mathematics, UBCCo-SupervisorRoger Beckie, Professor, Department of Earth, Ocean and Atmospheric Sciences, UBCSupervisory Committee MemberEldad Haber, Professor, Department of Earth, Ocean and Atmospheric Sciences, UBCUniversity ExaminerSteven Weijs, Associate Professor, Department of Civil Engineering, UBCUniversity ExaminerJasper Vrugt, Professor, Department of Civil and Environmental Engineering, University of Cali-fornia, IrvineExternal ExamineriiAbstractSince all ecological- and human-centric systems rely on a steady water supply, scientists must learnthe true-underlying processes that force hydrological behavior from a few thousand catchments withavailable data in order to transfer that knowledge to the infinite number of ungauged catchments.While hydrologists have historically preferred to utilize physics-based equations to gain and transferknowledge, data-driven methods for hydrological inference may provide more promising approaches.Due to the unique properties of hydrological data, such as extreme spatiotemporal depende…
has type: Thesis/Dissertation
date issued: 2025-11
is shown at: 10.14288/1.0450086
language: eng
degree discipline: Geological Sciences
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Ameli, Ali
contributor: Robeva, Elina
type: Text
identifier URI: http://hdl.handle.net/2429/92280