Short-term and long-term uncertainty analysis in groundwater level forecasting

Groundwater resources are essential to ensure future water security, especially in semi-arid areas such as the Mediterranean region. Groundwater level forecasting allows to predict the availability of the resource under different scenarios (including potential future climate change scenarios), although sometimes there is not enough monitoring data to develop distributed models. Some approaches such as lumped models and/or artificial intelligence algorithms have been demonstrated to provide satisfactory results by using a reduced amount of data.

In this work, we analyse the impact of some sources of uncertainty in the generation of local future groundwater level forecasts by using lumped models and artificial neural networks (ANN). The climate uncertainty is constrained from specific warming scenarios by removing the projections coming from inferior models (from multi-criteria analyses) taking into account their availability to reproduce historical climate statistics. A stochastic weather generator was used to generate multiple series of exogenous variables, which will allow to perform a stochastic forecast. The structural uncertainty related with the propagation of hydrological impact of ensembled climatic series is analysed by simulating with different lumped and ANN models.

The lumped models were calibrated through an automatic procedure. We also applied a sensitivity analysis in order to adjust the range of some hydrogeological parameters. Multiple configurations of ANN (approaches, number of neurons and delays) and exogenous variables were tested to select the best experiments by considering the mean value of MSE.

We analyse the climatic and structural uncertainty for short-term forecasting using both modelling approaches. We also analyse the long-term uncertainty by simulating with lumped models. The generation of stochastic predictions will be explored, by applying the Monte Carlo Method from the simulation with multiple selected models with good performance indicators.

The methodology was applied to the Campo de Montiel aquifer in central Spain, an area where groundwater and surface water are closely interconnected, with recognized Natural Park and Ramsar site such as Lagunas de Ruidera wetland, but also an intensive groundwater extraction due to the agricultural demand. This aquifer is essential as strategic water reserve under drought periods in a semi-arid climatic context.

The results have been also compared with those obtained with MODFLOW, showing the differences between distributed vs lumped approaches (sensitivity of the results to the spatial resolution of the methods).

 

Funding: This research was partially funded by the project SIGLO-PRO (PID2021- 128021OB - I00/ AEI / https://doi.org/10.13039/501100011033/FEDER,UE), from the Spanish Ministry of Science, Innovation and Universities.