A parsimonious methodological framework for short-term forecasting of groundwater levels

Groundwater plays a significant role as a strategic resource in reducing the impact of droughts. In spite of its impor-tance, there are still many groundwater bodies in which there is not enough monitoring data to define classic distrib-uted mathematical models to forecast future potential levels. The main aim of this study is to propose and evaluate a novel parsimonious integrated method for the short-term forecasting of groundwater levels. It has low requirements in term of data, and it is operational and relatively easy to apply. It uses geostatistics, optimal meteorological exogenous variables and artificial neural networks. We have illustrated our method in the aquifer Campo de Montiel (Spain). The analysis of optimal exogenous variables revealed that, in general, the wells with stronger correlations with precip-itation are located closer to the central part of the aquifer. NAR, which does not consider secondary information, is the best approach for 25.5 % of the cases and is associated with well locations with lower R2 between groundwater levels and precipitation. Amongst the approaches with exogenous variables, the ones that use effective precipitation have been selected more times as the best experiments. NARX and Elman using effective precipitation had the best ap-proaches with 21.6 % and 29.4 % of the cases respectively. For the selected approaches, we obtained a mean RMSE of 1.14 m in the test and 0.76, 0.92, 0.92, 0.87, 0.90, and 1.05 m for the forecasting test for months 1 to 6 respectively for the 51 wells, but the accuracy of the results can vary depending on the well. The interquartile range of the RMSE is around 2 m for the test and forecasting test. The uncertainty of the forecasting is also considered by generating multiple groundwater level series.

Automated summary

Groundwater plays a vital role in drought mitigation, but lack of monitoring data makes it challenging to forecast future levels accurately. This study proposes a novel and simple method using geostatistics, optimal exogenous variables, and artificial neural networks for short-term groundwater level forecasting. Analysis shows that wells closer to the central part of the aquifer have stronger correlations with precipitation. Among the approaches with exogenous variables, NARX and Elman models using effective precipitation were found to be the best in most cases. The accuracy of the forecasting results varies depending on the well, with an average RMSE of 1.14 m for the selected approaches.