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Methods for Characterizing Groundwater Resources with Sparse In-Situ Data

Nishimura, Ren

Titre : Methods for Characterizing Groundwater Resources with Sparse In-Situ Data

Auteur : Nishimura, Ren

Université de soutenance : Brigham Young University

Grade : Master of Science (MS) 2022

Résumé
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.

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Page publiée le 13 décembre 2022