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.

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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.

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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.

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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.

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The spatiotemporal monitoring of droughts is a complex task. In the past decades, drought monitoring has been increasingly developed, while the consideration of its spatio-temporal dynamics is still a challenge. This study proposes a method to build the spatial tracks and paths of drought, which can enhance its monitoring. The steps for the drought tracks calculation are (1) identification of spatial units (areas), (2) centroids localisation, and (3) centroids linkage. The spatio-temporal analysis performed here to extract the areas and centroids builds upon the Contiguous Drought Area (CDA) analysis. The potential of the proposed methodology is illustrated using grid data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor over India (1901-2013), as an example. The method to calculate the drought tracks allows for identification of drought paths delineated by an onset and an end in space and time. Tracks, severity and duration of the drought are identified, as well as localisation (onset and end position), and rotation. The response of the drought tracking method to different combinations of parameters is also analysed. Further research is in progress to set up a model to predict the drought tracks for particular regions across the world, including India (https://www.researchgate.net/project/STAND-Spatio-Temporal-ANalysis-of-Drought).@en

Drought is a complex natural phenomenon. The description of the way in which drought changes (moves) in space may help to acquire knowledge on its drivers and processes to improve its monitoring and prediction. This research presents the application of an approach to characterise the dynamics of drought. Tracks, severity, duration, as well as localisation (onset and end position), and rotation of droughts were calculated. Results of calculated droughts were compared with documented information. Data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor was used to identify droughts in India as an example for the period 1901–2013. Results show regions where droughts with considerable coverage tend to occur. Paths, i.e. consecutive spatial tracks, of six of the most severe reported droughts were analysed. In all of them, areas overlap considerably over time, which suggest that drought remains in the same region for a period of time. Results of this research are being used to build a model to predict the spatial drought tracks, incl. India (https://www.researchgate.net/project/STAND-Spatio-Temporal-ANalysis-of-Drought).

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