Combining Hydrology With Data Science and Economics to Address Modern Water Resources Challenges
Historically, hydrology was born out of the necessity to inform engineering (e.g., river engineering, water supply and urban drainage). It developed further as people noticed that water is at the intersection of many systems on the planet. Proper attribution of hydrologic change requires sufficient data, understanding of landscape processes, and often the construction of models at a range of scales. The world is rapidly changing due to climate and anthropogenic drivers that present new issues that need to be addressed. In that context, modern hydrology faces a range of fundamental and well-established challenges, three of which are addressed in this dissertation: (i) data scarcity, with only a few hydrological systems having in situ data globally, (ii) heterogeneity of hydrologic processes and variables and the need to provide high-resolution hydrologic information and data at local scales, while also generating consistent information and data at landscape scale, and (iii) feedback within water resource systems and determining cause and effect in observational data (e.g. feedback loops between human and natural systems). Recently, there have been promising opportunities to address these challenges through combining hydrology with complimentary (similarity in the approaches and methods) disciplines and is the focus of this dissertation.
Each chapter of this dissertation illustrates the promise of interdisciplinary approaches combining hydrology with other fields of study to address important challenges in the hydrologic sciences. General insight can be drawn from each study in this dissertation to address one or more of the three fundamental challenges. In the first study, I address the challenges of clouds and missing images in the determining of water extent in remote sensing. I found that the approach was robust to major deviations from most of its underlying assumptions. In the second study, I address the challenge of modelling wetlandscape processes, both hydrological and ecological, without full landscape in situ data. I investigate the potential to use water extent information from satellite imagery to calibrate landscape-scale process-based hydrological models. Compared to in situ observation of instrumented wetlands, satellite imagery encounters challenges in detecting water but it is not subject to the sampling errors associated with modelling arbitrary subsets of a heterogeneous landscape. In the third study, I address the challenge of modeling the common pool depletion of shared aquifers. I couple human decisions (game theory) with hydrogeological models of groundwater to exhibit user incentives to overpump in shared aquifers. The coupled model is analytically tractable, which allows it to be suitably non-dimensionalized. This, in turn, allowed it to be leveraged to draw generalized insights on how economic differences between aquifer users interact with their spatial configuration to determine incentives to over-pump.
History
Date Modified
2022-08-02Defense Date
2022-06-07CIP Code
- 14.0801
Research Director(s)
Marc F. MüllerCommittee Members
Alan Hamlet Diogo Bolster Bruce HuberDegree
- Doctor of Philosophy
Degree Level
- Doctoral Dissertation
Alternate Identifier
1338040068Library Record
6263202OCLC Number
1338040068Program Name
- Civil and Environmental Engineering and Earth Sciences