Groundwater is used worldwide as a major source for agricultural irrigation, industrial processes, mining, and drinking water. An accurate understanding of groundwater levels and trends is essential for decision makers to effectively manage groundwater resources throughout an aquifer, ensuring its sustainable development and usage. Unfortunately, groundwater is one of the most challenging and expensive water resources to characterize, quantify, and monitor on a regional basis. Data, though present, are often limited or sporadic, and are generally not used to their full potential to aid decision makers in their groundwater management.This thesis presents a solution to this under-utilization of available data through the creation of an open-source, Python-based web application used to characterize, visualize, and quantify groundwater resources on a regional basis. This application includes tools to extrapolate and interpolate time series observations of groundwater levels in monitoring wells through multi-linear regression, using correlated data from other wells. It is also possible to extrapolate time series observations using machine learning techniques with Earth observations as inputs. The app also performs spatial interpolation using GSLIB Kriging code. Combining the results of spatial and temporal interpolation, the app enables the user to calculate changes in aquifer storage, and to produce and view aquifer-wide maps and animations of groundwater levels over time. This tool will provide decision makers with an easy to use and easy to understand method for tracking groundwater resources. Thus far, this tool has been used to map groundwater in Texas, Utah, South Africa, Colombia, and the Dominican Republic.
College and Department
Ira A. Fulton College of Engineering and Technology; Civil and Environmental Engineering
BYU ScholarsArchive Citation
Evans, Steven William, "Groundwater Level Mapping Tool: Development of a Web Application to Effectively Characterize Groundwater Resources" (2019). Theses and Dissertations. 7738.
groundwater, aquifers, earth observations, machine learning, water resources management