Documentation | Sensor network & alarming | Hydrochemistry | Isotopes

The MEDSAL WEB OBSERVATORY serves as a graphical interface to the database compiled during the project.

The MEDSAL Project aims to secure the availability and quality of groundwater reserves in Mediterranean coastal areas, which are amongst the most vulnerable regions in the world to water scarcity and quality degradation. This will be addressed by providing a novel holistic approach, towards the sustainable management of coastal aquifers, which are affected by increased (single or multi-induced) groundwater salinization risk, especially under the variable meteo-climatic conditions of the Mediterranean and the rapidly changing socio-economic context. More information regarding the project, partners and news can be found on the official MEDSAL Project website.

Main goal

“The MEDSAL Project aims to secure the availability and quality of groundwater reserves in Mediterranean coastal areas, which are amongst the most vulnerable regions in the world to water scarcity and quality degradation. This will be addressed by providing a novel holistic approach, towards the sustainable management of coastal aquifers, which are affected by increased (single or multi-induced) groundwater salinization risk, especially under the variable meteo-climatic conditions of the Mediterranean and the rapidly changing socio-economic context.

Ambition

MEDSAL aims at developing innovative methods to identify various sources and processes of salinization and at providing an integrated set of modeling tools that capture the dynamics and risks of salinization. In this context, MEDSAL will provide a classification of groundwater salinization types for Mediterranean coasts and innovative methods to detect these types, also in complex karstic and data-scarce environments. These outcomes will be reached by better integration of hydrogeochemical and environmental isotope data with physical-based groundwater flow and transport models and advanced geostatistics. Artificial intelligence and deep learning methods will be also used to improve the detection of patterns in multi-dimensional hydrogeochemical and isotope data.

This project has been funded by PRIMA (Section 2 2018 programme) and the European Commission.