Evaluation of Groundwater Levels in the Arapahoe Aquifer Using Spatiotemporal Regression Kriging

Abstract

AbstractGroundwater monitoring is fundamental to understanding system dynamics, trends in storage, and the long‐term sustainability of an aquifer. Water‐level data are the key source of information used to understand the response. However, groundwater‐level data are often irregularly sampled, leading to temporal gaps in the record, and are not adequately distributed spatially across an aquifer. This presents challenges when spatially interpolating potentiometric surfaces and creating groundwater maps due to data availability. We present a spatiotemporal kriging methodology to improve spatial and temporal confidence in groundwater‐level predictions at unsampled locations. The space–time data set consists of a trend and residual component modeled with a linear regression and utilize a sum‐metric model to represent spatiotemporal covariances. The Arapahoe aquifer is used as a case study to demonstrate the benefits of spatiotemporal kriging over spatial kriging across a sparsely gauged and irregularly sampled aquifer. The Arapahoe aquifer is a major source of water for many residents along the Rocky Mountain Front Range in Colorado. The results show superior performance of spatiotemporal kriging to predict groundwater levels over the traditional spatial kriging. Spatiotemporal kriging represents realistic temporal and spatial changes in water levels and avoids some of the problems inherent to spatial kriging. This study demonstrates the power of spatiotemporal kriging to help inform system dynamics in irregularly sampled aquifers.