Mining activities depend significantly on water resources availability as it consists a major tool of the extraction, processing and the post closure mining operations. Especially, groundwater is the major water source in most mining areas. However, overexploitation, competition from the communities and climate change effects have caused significant stress on the groundwater resources in many areas of the Mediterranean basin. The sustainability of mining operations is threatened as well as the uninterrupted supply of raw materials to the industry. In this work spatial estimation and analysis of groundwater stress at hydrological basin-scale in the European part of the Mediterranean region is applied using local and global datasets. Aquifer productivity index and groundwater use information at monitoring sites are extracted from the River Basin Management Plans of the European Environment Agency, while groundwater recharge is considered from the World-wide Hydrogeological Mapping and Assessment Program (WHYMAP) after validation. The processing of these data using the Self Organized Maps technique and their integration within a novel function, provide the groundwater stress index. The output of this work can be used for governance and management decisions that will improve groundwater resources availability in vulnerable areas ensuring the sustainable use from the communities and the industry.

Precipitation data are useful for the management of water resources as well as flood and drought events. However, precipitation monitoring is sparse and often unreliable in regions with complicated geomorphology. Subsequently, the spatial variability of the precipitation distribution is frequently represented incorrectly. Satellite precipitation data provide an attractive supplement to ground observations. However, satellite data involve errors due to the complexity of the retrieval algorithms and/or the presence of obstacles that affect the infrared observation capability. This work presents a methodology that combines satellite and ground observations leading to improved spatiotemporal mapping and analysis of precipitation. The applied methodology is based on space-time regression kriging. The case study refers to the island of Crete, Greece, for the time period of 2010-2018. Precipitation data from 53 stations are used in combination with satellite images for the reference period. This work introduces an improved spatiotemporal approach for precipitation mapping.