Artificial neural networks (ANNs) have recently been used to predict the hydraulic head in well locations. In the present work, the particle swarm optimization (PSO) algorithm was used to train a feed-forward multi-layer ANN for the simulation of hydraulic head change at an observation well in the region of Agia, Chania, Greece. Three variants of the PSO algorithm were considered, the classic one with inertia weight improvement, PSO with time varying acceleration coefficients (PSO-TVAC) and global best PSO (GLBest-PSO). The best performance was achieved by GLBest-PSO when implemented using field data from the region of interest, providing improved training results compared to the back-propagation training algorithm. The trained ANN was subsequently used for mid-term prediction of the hydraulic head, as well as for the study of three climate change scenarios. Data time series were created using a stochastic weather generator, and the scenarios were examined for the period 2010–2020.

The Mediterranean is an area where the balance between water demand and abstractions vs. water availability is often under stress already, as demonstrated here with the Water Exploitation Index. In this work, model estimates on how different proposed measures for water resources management would affect different indicators. After a review of the current water resources status in the Mediterranean and the definition of indicators used in this study, aspects interlinked with water in the Water-Energy-Food-Ecosystems Nexus are briefly discussed, focusing on problems linked with water scarcity and depletion of groundwater resources as well as with climate change projections. Subsequently, the proposed measures for water efficiency are detailed—irrigation efficiency, urban water efficiency, water reuse and desalination—that might be effective to reduce the growing water scarcity problems in the Mediterranean. Their effects that result from the LISFLOOD model, show that wastewater reuse, desalination and water supply leakage reduction lead to decreased abstractions, but do not affect net water consumption. Increased irrigation efficiency does decrease consumption and reduces abstractions as well. We deduct however that the current envisaged water efficiency measures might not be sufficient to keep up with the pace of diminishing water availability due to climate change. More ambition is needed on water efficiency in the Mediterranean to keep water scarcity at bay.

As demand for more hydrological data has been increasing, there is a need for the development of more accurate and descriptive models. A pending issue regarding the input data of said models is the missing data from observation stations in the field. In this paper, a methodology utilizing ensembles of artificial neural networks is developed with the goal of estimating missing precipitation data in the extended region of Chania, Greece on a daily timestep. In the investigated stations, there have been multiple missing data events, as well as missing data prior to their installation. The methodology presented aims to generate precipitation time series based on observed data from neighboring stations and its results have been compared with a Multiple Linear Regression model as the basis for improvements to standard practice. For each combination of stations missing daily data, an ensemble has been developed. According to the statistical indexes that were calculated, ANN ensembles resulted in increased accuracy compared to the Multiple Linear Regression model. Despite this, the training time of the ensembles was quite long compared to that of the Multiple Linear Regression model, which suggests that increased accuracy comes at the cost of calculation time and processing power. In conclusion, when dealing with missing data in precipitation time series, ANNs yield more accurate results compared to MLR methods but require more time for producing them. The urgency of the required data in essence dictates which method should be used.