Dissertation
Advancing water resources decision processes: client-side web simulation for hydrology and novel approaches to incorporating social preference
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Spring 2025
DOI: 10.25820/etd.008030
Abstract
Decision making is the procedure to select an action from several alternative options based on a structured consideration of values, measures, and beliefs. In civil and environmental engineering, decision making shapes both the built and natural environments through infrastructure planning, resource allocation, and environmental intervention.
This is particularly true of the water environment, where water moves through and is affected by myriad, interconnected natural and built systems.
Though convenient to idealize decision processes in the water environment as ones based solely upon measurement and other “objective” criteria, decision making considers a complex tangle of sociotechnical measures and values that are both explicit and implicit, quantitative and qualitative. On top of this, human subjects -– e.g., forecasters, operators, first responders, etc. –- add further complexity as executors.
These complexities present major challenges to advancing decision-making systems, even as their individual components -- data collection, modeling, operations -— progress independently.
One area of substantial progress is environmental monitoring and control. Advances in both domains have introduced new and diverse data streams that comprise a growing environmental monitoring layer used to inform decision making.
Concurrently, novel control technologies are being deployed alongside sensors at an increasing rate.
The proliferation of new sensing and control locations increase the ability, and complexity, to actively manipulate the environment.
Likewise, the growing ability to manipulate the environment presents novel modes for decision makers to address persistent water resources challenges such as flooding and water quality.
Yet, despite these advances, the decision-making processes and workflows that rely on sensing and control technologies have not progressed at the same pace. Weaknesses in the connective tissue linking data to action, and action to outcome, continue to limit the effectiveness of these technologies within decision-making systems.
Advancing decision-making in water resources will require strengthening this connective tissue in both technological and social dimensions.
On one hand, bringing hydrologic simulation to the web —- particularly through client-side tools —- can expand access to modeling capabilities for a broader range of stakeholders.
Just as importantly, web-based simulation can integrate more seamlessly with the many components of environmental modeling workflows that are already online, enabling more connected, responsive, and scalable decision-support systems.
On the other, as algorithmic decision processes become more embedded in water infrastructure, it is critical to develop mechanisms that preserve and incorporate human values and preferences within these automated systems.
This dissertation explores two key areas necessary to support future decision systems: (1) the modelling foundation required for interactivity and accessibility, and (2) the incorporation of human preferences in automated decisions.
Current hydrologic simulation and water resources modelling frameworks remain siloed, non-interactive, and largely inaccessible through modern platforms. This technological limitation has slowed the evolution of user-centered, adaptive modeling workflows.
To address this, I developed a device-agnostic, generalized hydrologic modeling framework designed specifically for the web.
This framework is web-native, open-source, and capable of simulating lumped, semi-distributed, and fully-distributed hydrologic models entirely within a browser.
With this work, I complete a critical, previously missing link in the hydrologic modeling stack: a native, in-browser simulation engine that integrates seamlessly with the growing suite of web-based hydrologic resources.
While this contribution does not directly solve decision-making problems, it lays essential groundwork for building accessible, interactive, and interoperable systems that can.
In parallel with the technical contributions of this work, I investigated how social preferences and values can be meaningfully integrated into algorithmic decision-making for water systems. In one study, I collected large-scale human preference data through paired decision experiments and trained aggregate data models to reflect these preferences.
When applied to simulated decision scenarios, these models performed near-optimally in minimizing damages, suggesting they can effectively capture collective social values.
In a second study, I modified a canonical real-time control algorithm to account for the heterogeneous social vulnerability of communities, enabling stormwater infrastructure to prioritize more vulnerable areas of a sewershed during flood events.
Parameterizations of the canonical and socially aware algorithms were used to simulate the operation of a stormwater network over a typical year.
Analysis showed that the socially aware algorithms consistently outperformed the canonical across multiple metrics drawn from social welfare economics -— without sacrificing hydraulic performance.
Together, these studies demonstrate two promising pathways -- rule-based and data-driven -—
for embedding social considerations into automated water infrastructure decisions.
Taken together, these contributions aim to strengthen the connective infrastructure that supports water resources decision systems -— both in terms of technical capability and social responsiveness. The development of a web-native hydrologic modelling framework offers a foundational step toward more interactive, accessible, and modular modelling tools.
In parallel, the exploration of social values in algorithmic decision systems offers an initial proposal to operate the emerging phenomena of cyberphysical water networks more faithfully to community needs and ethical considerations.
Though distinct in approach, both research threads work toward a more responsive and equitable future for water resources management: one by building accessible modeling infrastructure, and the other by aligning automated decisions with community values.
This dissertation contributes a set of conceptual and technological tools that move toward more inclusive, adaptive, and connected decision-making practices in water resources management.
Details
- Title: Subtitle
- Advancing water resources decision processes: client-side web simulation for hydrology and novel approaches to incorporating social preference
- Creators
- Gregory Ewing
- Contributors
- Ibrahim Demir (Advisor)Allen Bradley (Committee Member)Jonathan L Goodall (Committee Member)Witold F Krajewski (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Civil and Environmental Engineering
- Date degree season
- Spring 2025
- DOI
- 10.25820/etd.008030
- Publisher
- University of Iowa
- Number of pages
- xvii, 195 pages
- Copyright
- Copyright 2025 Gregory Ewing
- Language
- English
- Date submitted
- 04/29/2025
- Description illustrations
- illustrations (some color)
- Description bibliographic
- Includes bibliographical references (page 127-157).
- Public Abstract (ETD)
Decision-making in water systems — such as how to manage floods or protect water quality — involves both complex data and human values. This dissertation explores how new technologies can help improve these decisions by making modeling tools more accessible and better aligned with community needs.
One part of the work focuses on building a new kind of hydrologic simulation tool that runs entirely in a web browser. This makes it easier for people — from scientists to city planners — to explore water models without needing specialized software. By bringing modeling to the web, this tool helps connect different parts of the decision-making process and makes it more interactive and inclusive.
The other part of the research looks at how human values can be included in automated decisions. In one study, community preferences were gathered to train data models that reflect what people care about in response to flood events. In another, a stormwater control algorithm was adjusted to give priority to more socially vulnerable areas. Both approaches showed it’s possible to improve fairness without sacrificing performance.
Together, this work takes early but important steps toward more accessible, responsive, and people-centered decision tools for managing water resources.
- Academic Unit
- Civil and Environmental Engineering
- Record Identifier
- 9984831121702771
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