Spatiotemporal Geostatistical Analysis of Groundwater Level in Aquifer Systems of Complex Hydrogeology

Abstract

Direct interpolation of groundwater levels often leads to contour maps which are hydrogeological inconsistent since numerical algorithms do not consider changes in flowline patterns caused by hydrogeological heterogeneities and aquifer boundary conditions. In the present work, this issue is assessed by conducting a geostatistical analysis based on Gaussian process method, using space‐time groundwater level observations, to generate reliable spatial maps of groundwater level variability and to identify groundwater level patterns over the island of Crete, Greece. Besides, two innovative tools are employed in the process: the Manhattan distance metric to obtain spatial correlation where faults are present in the aquifer and the spatiotemporal Spartan covariance function to obtain spatiotemporal interdependence. The results show significant prediction improvement over convectional geostatistical methods. Useful information is obtained from the delivered map notifying areas under high risk of groundwater resources shortage. The developed approach could be applied to other areas with analogous hydrogeological properties. It will be especially valuable in semi‐arid areas prone to droughts, where groundwater represents the main source of water.

Data‐Driven Estimation of Groundwater Level Time‐Series at Unmonitored Sites Using Comparative Regional Analysis

“…This type of approach requires few data on local system descriptors, while often long and measurement‐dense series of input signal and groundwater measurements are necessary to achieve good calibrations. In contrast to groundwater‐gradient driven methods, data‐driven methods either use spatio‐temporal geostatistics (e.g., Ruybal et al., 2019; Varouchakis et al., 2022) or transfer net precipitation input into groundwater level changes (Chen et al., 2002). However, available methods predict groundwater level only at monthly or annual resolution and consequently do not capture the large intra‐annual and intra‐monthly variability of groundwater dynamics (e.g., Heudorfer et al., 2019).…”

Data-driven Estimation of Groundwater Level Time-Series Using Comparative Regional Analysis

“…In contrast to groundwater-gradient driven methods, data-driven methods either use spatio-temporal geostatistics (e.g. Ruybal et al, 2019;Varouchakis et al, 2022) or transfer net precipitation input into groundwater level changes (Z. Chen et al (2002)).…”