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Methods for Characterizing Groundwater Resources with Sparse In-Situ Data

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.

Identiferoai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-10547
Date14 June 2022
CreatorsNishimura, Ren
PublisherBYU ScholarsArchive
Source SetsBrigham Young University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceTheses and Dissertations
Rightshttps://lib.byu.edu/about/copyright/

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