Asian Journal of Geographical Research

The application of mathematical trend surface modeling in combination with geostatistical methods has been demonstrated to be both a cost-effective and reliable approach for elucidating the geological characteristics of aquifer systems. This research introduces an innovative model for assessing the spatial variability of groundwater levels and the structural characteristics of associated aquifers. The model integrates geostatistical techniques with Geographic Information Systems (GIS), offering a robust framework for spatial analysis.  The study encompasses seven districts of the Bundelkhand region and emphasizes the critical role of trend surface modeling of groundwater levels and GIS in enhancing the understanding of regional hydrogeological conditions. The objective of this research is twofold: first, to develop a model for analyzing polynomial surface trends through the application of geostatistical methods to the groundwater levels; and second, to investigate the relationship between groundwater variability and mathematical surface trends. The geostatistical analysis involved fitting theoretical semi-variograms to experimental data to determine the nugget, continuity, sill, and range of influence. Kriging, using these parameters, generated predictive maps of groundwater levels along with associated uncertainty. Point kriging cross-validation (PKCV) parameters were optimal, acceptable, and confirmed the unbiasedness hypothesis of kriging. Based on the PKCV, five essential parameters were computed to accept the anisotropy semi-variogram fitted model. These are (1) kriging mean error (KME), ideally close to zero so that there are no over or underestimates, (2) goodness of fit (R2), (3) the ratio of estimated variance (EV) to kriging variance (KV) lies between 0.95-1.05, (4) good eye visualization fit and (5) significant t-test on the correlation coefficient.  The estimation of groundwater levels was facilitated by employing a second-order mathematical polynomial trend surface, with the resulting positive and negative residuals providing insights into the geology of the aquifer. A positive residual of the trend surface for the groundwater level variable is indicative of a favourable rock type for aquifer recharge, while a negative residual signifies an unfavourable rock type for aquifer recharge. In addition, the kriged surfaces for all seasons indicate severe groundwater depletion in the central zone of the study area. This methodology enhances the understanding of groundwater level fluctuations and provides a cost-effective approach for identifying critical areas in need of immediate, sustainable management interventions.