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 importance, there are still many groundwater bodies in which there is not enough monitoring data to define classic distributed 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 precipitation 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 approaches 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.

Assessment of chloride natural background levels by applying statistical approaches. Analyses of European coastal aquifers in different environments.

Estimated natural background levels (NBLs) are needed to assess groundwater chemical status according to the EU Groundwater Directive. They are commonly derived for different substances by applying statistical methodologies. Due to the complexity of the sea water intrusion process, some of those methods do not always provide appropriate assessment of chloride NBLs. This paper analyzes the applicability of different NBL estimation methods in five EU coastal aquifers with significant differences in available datasets and hydrogeological settings. A sensitivity analysis of results to different constraints was performed to remove samples with anthropogenic impacts. A novel statistical approach combining different methods to identify the range of chloride NBLs is proposed. In all pilots the estimated NBLs were below 85 mg/L and fitted well with previous studies and expert judgment, except Campina del Faro aquifer (the maximum being 167.5 mg/L). Although this approach is more time consuming, it provides a more robust solution.