This study presents a simple framework methodology for selecting the most appropriate locations for managed aquifer recharge (MAR). The proposed approach is applicable to aquifers that are located in coastal or mountainous areas and are used either for agricultural or industrial (e.g., mining) activities. A characteristic case study for the identification of the areas that are the most suitable for aquifer recharge using a GIS multi-criteria decision analysis (GIS-MCDA) method by means of MAR type spreading methods is the Geropotamos basin, Crete, Greece. Criteria combining a high relevance and high data availability, and providing unique information, were selected to assess the suitability of aquifer recharge in the basin. The criteria applied to evaluate the sites’ suitability for MAR spreading methods are hydrogeology, slope, land use, rainfall, groundwater level, soil texture and distance to source water. This study uses the ‘Pairwise comparison’ to assign criteria weights, as part of the Analytic Hierarchy Process (AHP), and examines four different scenarios. In all four scenarios, downstream areas, and close to the river Geropotamos, coincide as the most appropriate for aquifer recharge.

Successful modelling of the groundwater level variations in hydrogeological systems in complex formations considerably depends on spatial and temporal data availability and knowledge of the boundary conditions. Geostatistics plays an important role in model-related data analysis and preparation, but has specific limitations when the aquifer system is inhomogeneous. This study combines geostatistics with machine learning approaches to solve problems in complex aquifer systems. Herein, the emphasis is given to cases where the available dataset is large and randomly distributed in the different aquifer types of the hydrogeological system. Self-Organizing Maps can be applied to identify locally similar input data, to substitute the usually uncertain correlation length of the variogram model that estimates the correlated neighborhood, and then by means of Transgaussian Kriging to estimate the bias corrected spatial distribution of groundwater level. The proposed methodology was tested on a large dataset of groundwater level data in a complex hydrogeological area. The obtained results have shown a significant improvement compared to the ones obtained by classical geostatistical approaches.

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.