Improvement of groundwater level prediction in sparsely gauged basins using physical laws and local geographic features as auxiliary variables

This work aims to present new modeling tools that help to better monitor and predict the groundwater level in sparsely gauged basins. The working area is the Mires basin of Mesara valley in the island of Crete (Greece). Efficient groundwater management in the basin is crucial in light of regional climate change model estimates showing a substantial risk of desertification for Crete. We propose that the prediction of the hydraulic head spatial variability in Mires basin can be improved by incorporating in the trend the distance of the prediction points from a temporary river crossing the basin and a component based on the generalized Thiem’s equation for multiple wells as well as using the flexible Spartan semivariogram family to perform Residual Kriging. Our proposal is supported by the results of cross validation analysis. Our results are applicable to other unconfined aquifers.

► We incorporate physical laws and geographic measures in hydraulic head trend model.
► Thiem’s equation for multiple wells improves the hydraulic head spatial estimation.
► Accounting for distance of prediction points from river also improves the estimation.
► Spartan semivariogram leads to optimal kriging interpolation results.
► Geometric anisotropy is investigated using the Covariance Hessian Identity.