Abstract
Groundwater water resources must be accurately characterized in order to be managed sustainably. Due to the cost to install monitoring wells and challenges in collecting and managing in-situ data, groundwater data is sparse in space and time especially in developing countries. In this study we analyzed long-term groundwater storage changes with limited times-series data where each well had only one groundwater measurement in time. We developed methods to synthetically create times-series groundwater table elevation (WTE) by clustering wells with uniform grid and k-means-constrained clustering and creating pseudo wells. Pseudo wells with the WTE values from the cluster-member wells were temporally and spatially interpolated to analyze groundwater changes. We used the methods for the Beryl-Enterprise aquifer in Utah where other researchers quantified the groundwater storage depletion rate in the past, and the methods yielded a similar storage depletion rate. The method was then applied to the southern region in Niger and the result showed a ground water storage change that partially matched with the trend calculated by the GRACE data. With a limited data set that regressions or machine learning did not work, our method captured the groundwater storage trend correctly and can be used for the area where in-situ data is highly limited in time and space.
Degree
MS
College and Department
Ira A. Fulton College of Engineering and Technology; Civil and Environmental Engineering
Rights
https://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Nishimura, Ren, "Methods for Characterizing Groundwater Resources with Sparse In-Situ Data" (2022). Theses and Dissertations. 9538.
https://scholarsarchive.byu.edu/etd/9538
Date Submitted
2022-06-14
Document Type
Thesis
Handle
http://hdl.lib.byu.edu/1877/etd12369
Keywords
groundwater, aquifer, sustainable water resource management, sustainable water development, sparse data, temporal interpolation, spatial interpolation
Language
english