Water quality degradation is one of today's key environmental challenges. Once contaminated, its treatment becomes difficult and prohibitively costly. Therefore, efficient and sustainable groundwater management is essential to conserve this vital resource. This is particularly true for the Elalla-Aynalem catchment, which serves as the primary water source for Mekelle town. This study aimed to produce optimized maps of groundwater table depths and nitrate concentrations using geospatial models, based on 123 data points for groundwater levels and 71 data points for nitrate concentrations. Empirical Bayesian Kriging (EBK) proved to be the most effective interpolation technique for groundwater table depth, while Simple Kriging (SK) was the best choice for predicting nitrate concentration, as determined by the lowest root mean square error (RMSE). The ranking of interpolation models for groundwater table depths was EBK > SK > OK > UK > IDW > RBF > LPI, whereas for nitrate concentrations, the models ranked as SK > EBK > IDW > OK > UK > RBF > LPI. These results demonstrate that geostatistical methods outperformed deterministic interpolation techniques for both parameters. The northwest and central regions of the study area were found to have shallow groundwater table depths, areas that also exhibited high nitrate concentrations, likely due to their proximity to pollutant sources. This research provides valuable insights for promoting sustainable groundwater exploration and implementing effective measures to protect groundwater from contamination.

Analysis of historical and future land use/land cover (LULC) dynamics using spatiotemporal data is crucial for better management of natural resources and environmental monitoring. This study investigated LULC transformations over a span of 60 years (1984–2044) for the Giba basin in northern Ethiopia. ArcGIS and the Cellular Automata and Artificial Neural Network (CA-ANN) model were used to develop the historical (1984, 2004, 2014, and 2024) and projected future (2034 and 2044) LULC maps of the basin, respectively. The results show that LULC categories experienced shifts from one class to another by 35%, 33%, and 40% in 2004–2014, 2014–2024, and 2004–2024, respectively. During 1984–2024, the largest and smallest percentage of positive changes were observed in settlement (7700%) and shrubs and bushes (25%), which increased from negligible to 78 km² and from 1668 km² to 2082 km², respectively. Furthermore, barren land and forestland showed the largest (−80%) and smallest (−37%) declines, which decreased from 956 km² to 187 km² and from 164 km² to 103 km² during the same period, respectively. Overall, the last 40 years witnessed considerable changes to LULC dynamics in the Giba basin. Cropland, water bodies, and settlements showed a continuously increasing trend throughout the historical study period, while grassland exhibited a continuous decreasing trend. Results of the CA-ANN model showed that the majority of the LULC categories (including water body, forest, bushes and shrubs, grassland, and barren land) will decrease, except for a slight increase of cropland (+6%) and settlements (+16%), which is projected to increase from 2570 km² to 2733 km² and from 78 km² to 91 km², respectively, in the next two decades, from 2024 to 2044. In general, high population increase, changes in government policies, and armed conflicts were found to be the most influential driving factors of LULC changes in the basin.

Analysis of historical and future land use/land cover (LULC) dynamics using spatiotemporal data is crucial for better management of natural resources and environmental monitoring. This study investigated LULC transformations for the period of 60 years (1984-2044) in the Giba basin, northern Ethiopia. An ArcGIS and the Cellular Automata and Artificial Neural Network (CA-ANN) model were used to develop the past (1984, 2004, 2014, and 2024) and future (2034 and 2044) LULC maps of the basin, respectively. The results show that LULC categories experienced shifts from one class to other classes by 35%, 33%, and 40%, in 2004-2014, 2014-2024, and 2004-2024, respectively. During the 1984-2024 period, the smallest and largest percentage of positive changes were observed in settlement (7700%) and shrubs and bushes (25%), which increased from negligible to 78 km2 and from 1668 km2 to 2082 km2, respectively. Whereas barren land and forestland showed the largest (-80%) and smallest (-37%) decreasing rate during the same period. Results of the CA-ANN model showed that majority of the LULC categories (including water body, forest, bushes and shrubs, grassland, and barren land) will decrease except a slight increase of cropland (+6%) and settlements (+16%) in the future two decades during 2024-2044. In general, the Giba basin has undergone significant changes during the study period. High population increase, changes in government policies, and armed conflicts were found to be the most influential driving factors of changes in the basin.