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

Groundwater 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. Groundwater is an important water resource that requires monitoring to understand changes and ensure that adequate resources will exist in the future. However, monitoring data are often not collected at the same time and frequency, leading to challenges when creating groundwater maps due to data gaps. Traditionally, groundwater maps are created using data that are collected or grouped to about the same time period. However, this approach does not use past or future information to help create maps for the time period of interest and fill data gaps. In this study, we use a technique that examines where monitoring data are collected and when the data are collected and combines this information to better provide estimates about groundwater in areas and times with no direct measurements. The Arapahoe aquifer in Colorado is used as a case study to show the benefits of this approach. The results show how the approach used in this study leads to more accurate potentiometric surface maps and helps inform system dynamics which can be used to improve management of regional groundwater resources. Spatiotemporal kriging leverages data from space–time neighbors, allowing for more information to infer the kriging predictionsSpatiotemporal kriging allows estimation of groundwater levels during times when data are not availableThe approach highlights the problems and challenges of using observed data that varies throughout time to create spatial groundwater maps