Spatiotemporal Analysis of the Groundwater Level in Meoqui, Chihuahua, Mexico—Water for the Future in a Growing Area in the North Central Desert of Mexico

A variety of statistical techniques were assessed for their usefulness for analysing the pattern of geographical and temporal changes in groundwater levels in order to diagnose the water supply in Meoqui, Chihuahua, which is situated in dry North-Central Mexico. These included Facebook Prophet, Lasso, generalized linear regularized models (GLMNET), autoregressive-integrated moving average (ARIMA), and a hybrid approach that merged Prophet and XGBoost. This was conducted on the assumption that the levels varied during the year and that it was possible to perform statistical analyses that derived a model explaining the changes and allowing for spatiotemporal prediction. The data set encompassed the period between 2020 and 2025 and was obtained from the Junta Municipal de Aguas y Saneamiento (JMAS) in Meoqui. The data set consisted of eight wells from which water was extracted for human consumption. The ARIMA model was identified as the optimal method for generating predictions on a monthly and annual basis. Furthermore, an inverse distance weighted (IDW) interpolation approach was utilised to conduct a spatial analysis. This enabled the visualisation of the predicted spatiotemporal changes in groundwater levels. The mean overall level was determined to be 26 m ± 16, with a minimum of 3 m and a lower level of 64 m. Models were estimated, comprising a general model and models specific to each well type. The best model for general level was Facebook Prophet (MAE 6.31, MAPE 16.28, MASE 1.23, SMAPE 16.79, RMSE 7.25, R-sq 0.29). The Sen’s slope of the historic level was found to be 0.38 (p < 0.001), thus indicating a decline in the groundwater level. The spatiotemporal analysis indicated a monthly decline in water levels from February to August, followed by an improvement in levels until November, which were then maintained until January. The lowest levels were observed in the area associated with Well 5. The findings of this study offer valuable insights into the spatiotemporal patterns of groundwater in the region, which could inform the development of sustainable groundwater management policies.