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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.

The Sustainable Development Goals (SDGs) have been adopted by countries and companies, including mining companies around the world. The aim of this study is to investigate the degree of importance of the seventeen sustainable development goals (SDGs) on sustainable mining using a rough sets based decision making approach. This novel approach consists of three consecutive stages, namely a questionnaire (survey), data analyses, and SDGs classification. Firstly, a survey is conducted to receive a response from internationally experts across different countries. Each participant is asked to evaluate the importance of each SDG. Secondly, the analyses are carried out to make a distinction among groups of participants who respond similarly and discover viewpoints from the industry, academia, and non-governmental organizations. Finally, the degree of importance of each SDG for sustainable mining is found using a novel decision making approach including Ordinal Priority Approach (OPA) based on rough sets. The survey of the results indicated that for all the participants of the survey, independently of their background, the most important SDG for sustainable mining was “SDG8: Decent work and economic growth”, while the one perceived as the least important was “SDG14: Life below water”. The main objective of SDG8 is to promote economic growth through job opportunities and decent work for all. This in turn leads to a more sustainable, long-term economic growth. While all SDGs play an important role, the proposed rough sets based decision making method provided a rational and objective evaluation performance of their perceived priority in the mining sector.

Purpose The combination of desalination technology with renewable energy sources (RES) provides a sustainable approach for increasing potable water availability without imposing negative environmental effects. This paper aims to present the development of a platform, which is an internet-based tool integrating the design optimization of desalination systems with spatial modeling based on a geographic information system (GIS). Design/methodology/approach The proposed platform assists decision-makers to select the optimal location and configuration of both the energy- and water-related subsystems of desalination plants that are power-supplied by RES, such that the lifetime cost of the overall desalination plant is minimized. It enables to optimize the desalination plant site selection and sizing with various hybrid power supply (solar, wind, wave and electrical grid power systems) and desalination technologies combinations, while simultaneously exploiting spatial technologies in an internet-based GIS platform. Findings A pilot study for the optimal design of stand-alone and grid-connected desalination plants powered by RES is presented, which demonstrates the functionality and features of the proposed platform. It is also shown that a grid-connected desalination plant designed by the proposed software design tool exhibits significantly lower lifetime installation and maintenance costs compared to its stand-alone counterpart. Originality/value The proposed platform combines technological, scientific and industrial knowledge with information about societal/political conditions and geo-spatial technologies in a user-friendly graphical interface. Therefore, it provides a design tool enabling its users to secure water supply in a sustainable and economically viable manner.

Sfakia is a mountainous municipality located in the southwestern area of the island of Crete, including the southern part of the Lefka Ori Mountain, with an elevation ranging from sea level to over 2400 m. The mountainous massif mainly consists of carbonate rocks and intense karstic phenomena can be observed. The part of the area exceeding over the elevation of 600 m is characterized by high amounts of precipitation (1500 mm). Despite this high precipitation, the water availability in the area is low. This work aims to identify the factors that affect the water-resources availability in this area. In addition, issues related to proper water management are presented and discussed that will allow areas with similar characteristics to adapt to climate change. Our study identified the complex geological setting of the area that redirects water away of the hydrological basin (karsts) and the geomorphology that favors precipitation loss through runoff as the most important factors for the low availability of water resources. This paper attempts to create a base of discussion for similar cases worldwide by providing a framework of approaching scientifically analogous problems.

Summarization: The joint spatiotemporal modeling of aquifer level fluctuations provides a significant tool in the prediction of groundwater levels at unvisited locations. Two types of variogram functions, appropriately modified to incorporate physical information, are presented and compared under Space-time Residual kriging methodology using the Bayesian Bootstrap idea to quantify uncertainty. The spatiotemporal trend is approximated using a component of physical meaning. A recently developed Spartan type non-separable variogram function employs tools of physical meaning to enhance the efficiency and reliability of spatiotemporal geostatistical modeling in groundwater applications. In addition, the product-sum space-time variogram is applied and involves in the separable variogram functions a scale factor of hydraulic conductivity directional ratio. The proposed variogram functions involve a non-Euclidean distance metric and are mathematically valid (i.e., constitutes permissible models). Herein, the efficiency of the proposed tools is tested using groundwater level data from an alluvial unconfined aquifer. Both functions provide improved results compared to those of the Space-time Ordinary kriging. Between the two, the non-separable one has the most efficient performance. Παρουσιάστηκε στο: Journal of Hydrology

The purpose of this study is to examine the uncertainty of various aspects of a combined artificial neural network (ANN), kriging and fuzzy logic methodology, which can be used for the spatial and temporal simulation of hydraulic head in an aquifer. This simulation algorithm was applied in a study area in Miami – Dade County, USA. The percentile methodology was applied as a first approach in order to define the ANN uncertainty, resulting in wide prediction intervals (PIs) due to the coarse nature of the methodology. As a second approach, the uncertainty of the ANN training is tested through a Monte Carlo procedure. The model was executed 300 times using different training set and initial random values, and the training results constituted a sensitivity analysis of the ANN training to the kriging part of the algorithm. The training and testing error intervals for the ANNs and the kriging PIs calculated through this procedure can be considered narrow, taking into consideration the complexity of the study area. For the third and final approach used in this work, the uncertainty of kriging parameter was calculated through the Bayesian kriging methodology. The derived results prove that the simulation algorithm provides consistent and accurate results.

The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space–time modeling and prediction of precipitation on Crete island in Greece. The proposed space–time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10–2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space–time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area.

This work carries the social learning process out via Living Labs in order to construct a common vision on sustainable groundwater management. In this process, the scientific and local knowledge are integrated. This study is part of Sustain-COAST project co-funded by PRIMA programme. Stakeholders’ active engagement is realized via Living Labs, which are participatory actions that encourage the dialogue among private and public actors, create institutionalized space for discussion and vision sharing, and analyze the stakeholder-suggested mitigation options.A stakeholder mapping took place, that is the list of all the key groups, organizations, and people involved to water management in the study area. Further analysis was carried out to better understand stakeholders’ roles and perspectives, within the first Living Lab, organized in Malia. 55 stakeholders interacted gathered, including water users, policy makers, local and regional authorities, water management and supply associations, socio-ecological and cultural associations, NGOs, citizens, technicians, external experts, scientists.Stakeholders got involved in social learning actions, knowing each other, expressed their motivations and expectations to participate in the first Living Lab and the project. Afterwards, a participatory session followed by implementing digital ICT tools (Mentimeter App.), which is an opinion survey technique that might improve societal awareness and stakeholders’ active engagement in water management. Afterwards, an interactive participatory map activity took place, which enabled the study site’s characterization according to key-stakeholders’ perception, knowledge, and expertise on water management issues in the area. Stakeholders collaborated in groups and filled maps of the study area with significant spatial data and information. Participants were asked to express their common vision on Malia in an entertaining puzzle activity.The aforementioned interactive sessions enabled the extraction of the raised water issues in Malia as well as the suggestion of possible options . The need for sustainable and balanced development taking into account principles of law and equal accessibility for all was specifically noted by stakeholders. Stakeholders evaluated the Living Labs as an innovative interactive and interesting way of exchanging views among institutions and citizens, through participation and technological means. Living Labs are expected to provide significant information exchange among institutions and actors and provide realistic and socially acceptable suggestions for the local community.Stakeholders are directly involved and motivated to maintain their active engagement in a long-lasting process via future Living Labs in Malia. Such actions increase governance capacity by addressing people’s skills in jointly decision-making and engaging stakeholders in a social learning process through participation. Actions that encourage dialogue among different actors and use innovative mediation techniques form the best options to improve and integrate water governance. Keywords: Living Labs; Innovative governance; Water resources management; Stakeholder mapping; Social learning processes; Stakeholders’ engagement The PRIMA programme is an Art.185 initiative supported and funded under Horizon 2020, the European Union’s Programme for Research and Innovation.The project is funded by the General Secretariat for Research and Technology of the Ministry of Development and Investments under the PRIMA Programme. PRIMA is an Art.185 initiative supported and co-funded under Horizon 2020, the European Union’s Programme for Research and Innovation.

In the Mediterranean region, climatic variations in conjunction to intensive agriculture deteriorates groundwater resources which are over-exploited to cover irrigation demands. A characteristic example is Messara Basin in the island of Crete, Greece. This work presents an integrated suitability assessment study for potential aquifer recharge that considers the availability of water resources, hydro-geological and geomorphological characteristics, climatic scenarios, soil properties and suitability, and socioeconomic analysis under the framework of a suitable aquifer recharge technique.The most suitable technique for planning the aquifer recharge was selected according to the area characteristics. The spreading method was assessed as the most suitable technique based on the area characteristics that should typically have a source of excess water available nearby, be located in a relatively flat area with permeable soils and be underlined by an unconfined aquifer. A multi-criteria decision analysis method was applied to identify suitable sites for implementing aquifer recharge type spreading method. The methodology is based on a multicriteria matrix developed in accordance to a relative optimization (weighting) method in terms of hydrogeological and geomorphological criteria, and water availability (reservoir, river). Criteria combining a high relevance and high data availability, and providing unique information, selected to assess the suitability of aquifer recharge in Messara basin such as slope, land use, hydrogeology, rainfall, groundwater level, soil texture and distance to source water.All the aforementioned factors were separately studied and analyzed and then were combined under the principles of the spreading method to provide by means of spatial maps the most appropriate locations in the study basin.The outcome of this work is a simple framework methodology for selecting the most suitable recharge locations of the underlying aquifers and to demonstrate its socioeconomic and environmental advantages. The results of this work will assist local authorities to consider the applicability of aquifer recharge in the Messara valley while it consists a framework for efficient planning of similar applications in other Mediterranean regions. AcknowledgmentsThis work was implemented in the framework of the research project Uncertainty-aware intervention design for Mediterranean aquifer recharge. The project: "Uncertainty-aware intervention design for Mediterranean aquifer recharge benefits from the support of the Prince Albert II foundation". http://www.fpa2.org ReferencesSpecial water secretariat of Greece, 2017. Integrated Management Plans of the Greek Watersheds, Ministry of Environment & Energy, Athens.Varouchakis, E.A., 2016. Integrated Water Resources Analysis at Basin Scale: A Case Study in Greece. J. Irrig. Drain. E-ASCE 142(3), 05015012. DOI:10.1061/(ASCE)IR.1943-4774.0000966

In the last few years, the island of Crete (Greece - Eastern Mediterranean) has been affected by extreme events. In recent decades, hydrometeorological processes in the island of Crete are monitored by an extensive network of meteorological stations. Here we stochastically analyze the spatial stochastic structure of precipitation in the island by employing sophisticated statistical tools, as well as by analyzing a large database of daily precipitation records. We investigate fifty-eight rainfall stations scattered in the four prefectures of Crete, for the years 1974-2020. Descriptive statistical analysis of precipitation examines several temporal properties in the data, while correlation analysis of precipitation variability provides relations between stations and regions for spatial patterns identification. This work also investigates the precipitation variability by employing statistical tools such as the autocorrelation, autoregressive (seasonal) analysis, probability distribution function fitting, and climacogram calculation, i.e. variance of the averaged process vs. spatial and temporal scales, to identify statistical properties, temporal dependencies, potential similarities in the dependence structure and marginal probability distribution.

Groundwater resources in Mediterranean coastal aquifers are under threat due to overexploitation and climate change impacts, resulting in saltwater intrusion. This situation is deteriorated by the absence of sustainable groundwater resources management plans. Efficient management and monitoring of groundwater systems requires interpreting all sources of available data. This work aims at the development of a set of plausible 3D geological models combining 2D geophysical profiles, spatial data analytics and geostatistical simulation techniques. The resulting set of models represents possible scenarios of the structure of the coastal aquifer system under investigation. Inverted resistivity profiles, along with borehole data, are explored using spatial data science techniques to identify regions associated with higher uncertainty. Relevant parts of the profiles will be used to generate 3D models after detailed Anisotropy and variogram analysis. Multidimensional statistical techniques are then used to select representative models of the true subsurface while exploring the uncertainty space. The resulting models will help to identify primary gaps in existing knowledge about the groundwater system and to optimize the groundwater monitoring network. A comparison with a numerical groundwater flow model will identify similarities and differences and it will be used to develop a typical hydrogeological model, which will aid the management and monitoring of the area's groundwater resources. This work will help the development of a reliable groundwater flow model to investigate future groundwater level fluctuations at the study area under climate change scenarios. This work was developed under the scope of the InTheMED project. InTheMED is part of the PRIMA programme supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 1923.

The continuous search for affordable and renewable energy resources is a topic of interest for decades. Many large-scale measuring campaigns have been conducted and various different tools have been developed over the years (both numerical and statistical in nature), in order to locate regions with high wind, wave and solar energy potential. Depending on the energy resource, not all regions are performing equally, as expected. To pinpoint regions with high energy gain requires state-of-the-art tools and unremitting research efforts.The objective of the current research effort is the spatio-temporal wave data analysis, originated from satellite data, and sensor buoy data scattered in the Aegean and Ionian Sea, with the use of geostatistical and dynamic downscaling methods, for estimating the wave energy potential for the Hellenic region. The main areas of interest are the Aegean and Ionian islands, with unsustainable energy production.WRF model is used to dynamically downscale coarse global climate model output to provide the regional wind forcing for a 40-year hindcast period on a 3 x 3 km grid over the Aegean and Ionian Seas. The calculated wind forcing is used as a driver for the WAVEWATCH-III wave model to calculate the significant wave height and period in the region and subsequently achieve a high-resolution estimation of the wave energy potential spatial distribution and temporal evolution. Model results have been validated with mooring time series of wave parameters in the Aegean Sea and satellite-based along track Significant Wave Height data available through CMEMS Wave Thematic Assembly Center (CMEMS WAVE TAC). To strengthen the results outcome, a spatio-temporal geostatistical methodology has been introduced to validate the computational results and provide a fast and robust estimation of the wave and energy fields. The results between the two different approaches are compared in order to establish either spatial or temporal correlation patterns. AcknowledgementsThis project has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No [1237].

In recent years, Artificial Neural Networks (ANNs) have proven their merit in being able to simulate the changes in groundwater levels, using as inputs other parameters of the water budget, e.g. precipitation, temperature, etc.. In this study, ANNs have been used to simulate hydraulic head in a large number of wells throughout the Danube River Basin, taking as inputs, precipitation, temperature, and evapotranspiration data in the region. Different ANN architectures have been examined, to minimize the simulation error of the testing data-set. Among the different training algorithms, Levenberg-Marquardt and Bayesian Regularization are used to train the ANNs, while the different activation functions of the neurons that were deployed include tangent sigmoid, logarithmic sigmoid and linear. The initial application comprised of data from 128 wells between 1 January 2000 and 31 October 2014. The best performance was achieved by the algorithm Bayesian Regularization with a error of the order based on all observation wells. A second application, compared the results of the first one, with the results of an ANN used to simulate a single well. The pros and cons of the two approaches, and the synergies of using both of them is further discussed in order to distinguish the differences, and guide researchers in the field for further applications.

Over the last decade, a lot of work has been performed to develop wind-wave potential prediction techniques that, effectively and within realistic time-frames, map the local climatology of a specific region. The combination of local and satellite climatic data, has been used, for good reason, in many wind-related-projects as it is linking mesoscale meteorology, with microclimatic weather phenomena. This way the driving geostrophic winds are effectively taken into account for the estimation of low altitude wind distribution.Using dynamical downscaling methodology, a nesting technique with 1/3 ratio is applied to downscale the raw computational grid of the satellite input data to a finer 3 x 3 Km results grid. This way, higher computational accuracy is achieved over the investigated regions, thus revealing finer wind scales phenomena. For the computational simulations, two different models have been used in order to generate meteo-climate parameters suitable for sea-wave results calculation: a dynamical downscaling model (at regional scale), and a wave model. The first model performs the downscaling of the satellite meteorological data in higher resolution grids for a wide area of the Aegean Sea. The produced fine grid output drives the later wave model in order to estimate the significant wave height and period over the areas of interest. The produced results will later be used in conjunction with novel geostatistical techniques to estimate the wave energy potential distribution in the Aegean and Ionian Sea. AcknowledgementsThis project has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No [1237].

In geostatistical analysis a Bayesian approach has more advantages over classical methods since it allows to deal with the parameters and the uncertainty in the model. Spatiotemporal geostatistical modelling can be performed by using the Gaussian process regression method under a Bayesian framework. In a Bayesian approach the overall uncertainty can be represented by a probability distribution. In this work the groundwater level spatiotemporal variability was assessed based on a ten years’ time series of biannual average data from an extensive network of wells in the island of Crete, Greece. The Gaussian process regression method was employed to produce reliable maps of groundwater level variability and to identify groundwater level patterns for the island of Crete. Thus, this work could help to detect areas where interventions of groundwater management are necessary considering the associated uncertainty.

Summarization: Analysis of rainfall data from the island of Crete, Greece was performed to identify key hydrological years and return periods as well as to analyze the inter-annual behavior of the rainfall variability during the period 1981–2014. The rainfall spatial distribution was also examined in detail to identify vulnerable areas of the island. Data analysis using statistical tools and spectral analysis were applied to investigate and interpret the temporal course of the available rainfall data set. In addition, spatial analysis techniques were applied and compared to determine the rainfall spatial distribution on the island of Crete. The analysis presented that in contrast to Regional Climate Model estimations, rainfall rates have not decreased, while return periods vary depending on seasonality and geographic location. A small but statistical significant increasing trend was detected in the inter-annual rainfall variations as well as a significant rainfall cycle almost every 8 years. In addition, statistically significant correlation of the island’s rainfall variability with the North Atlantic Oscillation is identified for the examined period. On the other hand, regression kriging method combining surface elevation as secondary information improved the estimation of the annual rainfall spatial variability on the island of Crete by 70% compared to ordinary kriging. The rainfall spatial and temporal trends on the island of Crete have variable characteristics that depend on the geographical area and on the hydrological period. Presented on: Acta Geophysica

The spatial variability evaluation of the water table of an aquifer provides useful information in water resources management plans. Geostatistical methods are often employed to map the free surface of an aquifer. In geostatistical analysis using Kriging techniques the selection of the optimal variogram is very important for the optimal method performance. This work compares three different criteria to assess the theoretical variogram that fits to the experimental one: the Least Squares Sum method, the Akaike Information Criterion and the Cressie’s Indicator. Moreover, variable distance metrics such as the Euclidean, Minkowski, Manhattan, Canberra and Bray-Curtis are applied to calculate the distance between the observation and the prediction points, that affects both the variogram calculation and the Kriging estimator. A Fuzzy Logic System is then applied to define the appropriate neighbors for each estimation point used in the Kriging algorithm. The two criteria used during the Fuzzy Logic process are the distance between observation and estimation points and the groundwater level value at each observation point. The proposed techniques are applied to a data set of 250 hydraulic head measurements distributed over an alluvial aquifer. The analysis showed that the Power-law variogram model and Manhattan distance metric within ordinary kriging provide the best results when the comprehensive geostatistical analysis process is applied. On the other hand, the Fuzzy Logic approach leads to a Gaussian variogram model and significantly improves the estimation performance. The two different variogram models can be explained in terms of a fractional Brownian motion approach and of aquifer behavior at local scale. Finally, maps of hydraulic head spatial variability and of predictions uncertainty are constructed for the area with the two different approaches comparing their advantages and drawbacks.

Summarization: The present research work applies ordinary kriging using an R programming script to map the free surface of an unconfined alluvial aquifer, calculating, at the same time, the standard deviation of the estimations. Moreover, indicator kriging is also applied to calculate the probability of the aquifer level to lie below a certain limit that could cause a significant impact on groundwater resources availability. Kriging efficiency depends on the estimation of the optimal spatial dependence of the measurements. Therefore, classical variogram functions and the Matérn model are applied to determine the spatial correlation of the measurements. The power-law variogram application provided the most accurate results. Maps associated with the hydraulic head spatial variability and prediction uncertainty, as well as a probability map were produced. The source R script is available with this work for public use. The concept, tools, and results of this work can be a useful framework for stakeholders and local authorities to design and decide optimal water resources management and governance. Presented on: Journal of Hazardous, Toxic, and Radioactive Waste

Summarization: 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). Presented on: Advances in Water Resources

Summarization: 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). Presented on: Science of the Total Environment

Reliable temporal modelling of groundwater level is significant for efficient water resources management in hydrological basins and for the prevention of possible desertification effects. In this work we propose a stochastic method of temporal monitoring and prediction that can incorporate auxiliary information. More specifically, we model the temporal (mean annual and biannual) variation of groundwater level by means of a discrete time autoregressive exogenous variable (ARX) model. The ARX model parameters and its predictions are estimated by means of the Kalman filter adaptation algorithm (KFAA) which, to our knowledge, is applied for the first time in hydrology. KFAA is suitable for sparsely monitored basins that do not allow for an independent estimation of the ARX model parameters. We apply KFAA to time series of groundwater level values from the Mires basin in the island of Crete. In addition to precipitation measurements, we use pumping data as exogenous variables. We calibrate the ARX model based on the groundwater level for the years 1981 to 2006 and use it to predict the mean annual and biannual groundwater level for recent years (2007–2010). The predictions are validated with the available annual averages reported by the local authorities.

In the present work, a simulation-optimization method is employed in order to manage saltwater intrusion in two unconfined coastal aquifers in Crete, Greece. The optimization formulation seeks to maximize groundwater withdrawal rates while maintaining the saltwater intrusion front at the current location or inhibiting it at locations closer to the coast. A combination of a groundwater flow model (MODFLOW) with the Ghyben-Herzberg saltwater front approximation and a sequential implementation of the Simplex algorithm (GWM) are employed. The results show that under the current pumping strategies, the saltwater intrusion front will continue to move inland, posing a serious threat to the groundwater quality of these regions. Optimal groundwater withdrawal scenarios that take into consideration the water needs of the local communities and environmental concerns are presented and discussed. In both case studies, significant reductions in pumping are required in order for the saltwater intrusion front to retract closer to the shoreline.

Soil salinity is a major soil degradation threat especially for arid coastal environments where it hinders agricultural production, thus imposing a desertification risk. In the prospect of a changing climate, soil salinity caused by brackish water irrigation introduces additional uncertainti

Summary Decision making is a significant tool in water resources management applications. This technical note approaches a decision dilemma that has not yet been considered for the water resources management of a watershed. A common cost-benefit analysis approach, which is novel in the risk analysis of hydrologic/hydraulic applications, and a Bayesian decision analysis are applied to aid the decision making on whether or not to construct a water reservoir for irrigation purposes. The alternative option examined is a scaled parabolic fine variation in terms of over-pumping violations in contrast to common practices that usually consider short-term fines. The methodological steps are analytically presented associated with originally developed code. Such an application, and in such detail, represents new feedback. The results indicate that the probability uncertainty is the driving issue that determines the optimal decision with each methodology, and depending on the unknown probability handling, each methodology may lead to a different optimal decision. Thus, the proposed tool can help decision makers to examine and compare different scenarios using two different approaches before making a decision considering the cost of a hydrologic/hydraulic project and the varied economic charges that water table limit violations can cause inside an audit interval. In contrast to practices that assess the effect of each proposed action separately considering only current knowledge of the examined issue, this tool aids decision making by considering prior information and the sampling distribution of future successful audits.

Classical geostatistical analysis deals with spatial data variability. Geostatistical modeling, however, often needs to address variables that change in space as well as in time, such as groundwater level or pollution transport in air or porous media. When data are distributed through time and space, a major advantage is that increased amounts of data support parameter estimation and prediction. In a statistical context, these data can be considered as random fields spread out in space and evolving in time (space–time random fields). Usually, spatiotemporal geostatistical analysis can be performed using the space–time ordinary kriging (STOK) method and joint space–time covariance functions. A space–time experimental variogram is required from the available level time series at different locations. The experimental variogram can be modeled with separable or nonseparable variogram functions. This chapter presents extensively the background of spatiotemporal geostatistical analysis in terms of STOK. In addition, a characteristic application is presented that assesses the spatiotemporal variability of an aquifer.

Geostatistics has been well established and developed during the last three decades and is widely applied in environmental research and technology. Geostatistics is a subdiscipline of spatial statistics. It includes a set of statistical methods that concern random variables with spatial and/or temporal variability (random fields). These variables represent physical quantities with economic or environmental importance. The methods are based on the assumption that the spatiotemporal variability includes a random component that has space–time correlation. Therefore statistical measures such as mean value, variance, standard deviation, spatiotemporal dependence, etc., are used to extract any useful information from the available data. Geostatistics deals with distributions in which the spatial and/or temporal dependence is the primary characteristic. Geostatistical analysis aims to estimate the statistical parameters that determine the spatial and/or temporal distribution and dependence of the relevant variables. This procedure is called parameter inference. These parameters are used to estimate (interpolate) the variables at desired spatiotemporal locations where no measurements are available. This chapter describes the basic mathematical background of geostatistics and presents a complete methodological guide for geostatistical analysis of physical variables.

Este artículo contiene 16 páginas, 6 figuras, 2 tablas., Soil degradation by water is a serious environmental problem worldwide, with specific climatic factors being the major causes. We investigated the relationships between synoptic atmospheric patterns (i.e. weather types, WTs) and runoff, erosion and sediment yield throughout the Mediterranean basin by analyzing a large database of natural rainfall events at 68 research sites in 9 countries. Principal Component Analysis (PCA) was used to identify spatial relationships of the different WTs including three hydro-sedimentary variables: rainfall, runoff, and sediment yield (SY, used to refer to both soil erosion measured at plot scale and sediment yield registered at catchment scale). The results indicated 4 spatial classes of rainfall and runoff: (a) northern sites dependent on North (N) and North West (NW) flows; (b) eastern sites dependent on E and NE flows; (c) southern sites dependent on S and SE flows; and, finally, (d) western sites dependent on W and SW flows. Conversely, three spatial classes are identified for SY characterized by: (a) N and NE flows in northern sites (b) E flows in eastern sites, and (c) Wand SW flows in western sites. Most of the rainfall, runoff and SY occurred during a small number of daily events, and just a few WTs accounted for large percentages of the total. Our results confirm that characterization by WT improves understanding of the general conditions under which runoff and SY occur, and provides useful information for understanding the spatial variability of runoff, and SY throughout the Mediterranean basin. The approach used here could be useful to aid of the design of regional water management and soil conservation measures., This research was supported by projects funded by the MINECOFEDER: CGL2014-52135-C3-3-R, CGL2014-59946-R, CGL2015-65569- R, CGL2015-64284-C2-2-R, and PCIN-2017-061/AEI. When this manuscript was first submitted Estela Nadal-Romero and Damià Vericat received a “Ramón y Cajal” postdoctoral contract (RYC-2013-14371 and RYC‐2010‐06264, Spanish Ministry of Economy and Competitiveness, MEC). Damià Vericat is now a Serra Húnter Fellow at the University of Lleida. María Fernández-Raga received a “José Castillejo” postdoctoral grant (Spanish Ministry of Education, Culture and Sports). Carla Ferreira was supported by a post-doctoral research grant from the Portuguese Science and Technology Foundation (SFRH/ BPD/120093/2016). Mariano Moreno-de las Heras received a Juan de la Cierva postdoctoral contract (IJCI-2015-26463) from the MEC. José Andrés López-Tarazón received a Vicenç Mut postdoctoral fellowship from the Autonomous Balearic Government (CAIB PD/038/2016). José Andrés López-Tarazón and Ramon Batalla also acknowledge the Secretariat for Universities and Research of the Department of the Economy and Knowledge of the Autonomous Government of Catalonia for supporting the Consolidated Research Group 2014 SGR 645 (RIUSFluvial Dynamics Research Group), and the CERCA Programme. This paper has benefited from the Lab and Field Data Pool created within the framework of the COST action CONNECTEUR (ES1306).

Summarization: Based on the predictions of General Circulation Models, significant reduction of precipitation in Mediterranean areas is a possible scenario. Hence, better understanding of the spatial and temporal precipitation patterns is necessary in order to quantify desertification risks and design suitable mitigation measures. This study uses monthly precipitation measurements from a sparse network of 54 monitoring stations on the Mediterranean island of Crete (Greece). The study period extends from 1948 to 2012. The data reveal strong correlations between the western and eastern parts of the island. However, the average annual precipitation in the West is about 450 mm higher than that in the East. We construct a spatial model of average annual precipitation in Crete. The model involves a topographic trend and residuals with anisotropic spatial correlations which are fitted with a recently developed variogram function. We use regression kriging to generate annual precipitation maps and to identify locations of high estimation uncertainty. To our knowledge, this is the most detailed spatial analysis of precipitation in Crete to date. We present the analysis in detail for the year 1971. The trend accounts for ≈ 74% of the total variance. The highest precipitation estimate is 2331 mm in the West and 1781 mm in the East. The highest relative estimation uncertainty (≈ 20%) is observed along the southeastern coastline of the island, where the lowest values of annual precipitation are observed. This region includes one of the major agricultural areas of the island. The same overall patterns are found for other years in the study. Finally, we find no statistical evidence for a decrease in the global (over the entire island) annual precipitation during the study period. Παρουσιάστηκε στο: Environmental Monitoring and Assessment

The purpose of this study is to examine the uncertainty of a combined artificial neural network (ANN), kriging and fuzzy logic methodology, which can be used for spatial and temporal simulation of hydraulic head in an aquifer. This methodology was applied in the past, while the verification of the model was performed by implementing it in a new study area, in Miami – Dade County, FL, USA. The percentile methodology was applied as a first approach in order to define the ANN uncertainty. As a second approach, the uncertainty of the ANN training is tested through a Monte Carlo procedure. The model was executed 300 times using different training set and initial random values each time. The training results constituted a sensitivity analysis of the ANN training to the kriging part of the algorithm. The training and testing error intervals for the ANNs and the kriging prediction intervals calculated through this procedure can be considered narrow compared to the complexity of the study area. For the third and final approach used in this work, the uncertainty of kriging parameter was calculated through the Bayesian kriging methodology. The results derived can prove that the simulation algorithm can provide consistent and accurate results.

The present research work uses Residual Kriging to estimate the groundwater level of an unconfined alluvial aquifer, as well as the trend function. The ground surface elevation is used as auxiliary variable in the trend model. Indicator Kriging is applied to detect potential vulnerable locations. Classical variogram functions are applied to determine the spatial correlation of the measurements. The risk of hydraulic head to lie below a threshold value is significant, mainly at the South and North parts of the aquifer, where the lower values of groundwater level are estimated, indicating that these areas require intense monitoring to ensure the water resources availability.

Δημοσίευση σε επιστημονικό περιοδικό Summarization: Recent climate change studies for the Mediterranean region have projected the near future temperature and precipitation trends. The increasing water demands for human consumption and agricultural purposes, as well as the potential overexploitation of the groundwater resources, in combination with the climatic projections are expected to affect the groundwater resources of the Mediterranean hydrological basins. This work focuses on the Viannos alluvial basin at the island of Crete, Greece. It considers primarily the increasing water demands for irrigation in the area of interest and the projected precipitation trend in the next five hydrological years for the island of Crete. The groundwater system is simulated based on the current hydrological/hydrogeological conditions of the basin and based on anticipated hydrological events. Scenarios concerning future precipitation trends and pumping schemes at existing wells are examined to assess the near-future stresses on the basin groundwater resources. Groundwater flow modeling is performed using the Visual Modflow software. Based on the study of different scenarios modeling results show that the aquifer is primarily affected during the dry period of the hydrological year. However, sufficiently replenishment is observed during the wet period as the highest water table drop is 0.65 m at the end of the 5 years modelling period. Therefore, the aquifer is not expected to face serious problems in the near future from the increased irrigation demands and from the short-term projected precipitation trends. Simulations for 10 and 20-year periods following the precipitation projections and the specified pumping schemes also show that the aquifer resources are not expected to be significantly affected. Presented on: Environmental Earth Sciences

Summarization: We conduct a spatiotemporal geostatistical analysis of groundwater level data using space–time Residual Kriging (STRK). We compare the performance of three spatiotemporal variogram models in mapping groundwater level data. All models involve the same space–time trend function; the first model is a separable product variogram that involves Matérn space and time functions, while the second and third use non-separable functions. The non-separable models include a diffusive space–time variogram and a spatiotemporal extension of the Spartan variogram. The non-separable models are applied for the first time to groundwater level data. Our analysis shows that the Spartan and the non-separable diffusive variograms fit the experimental variogram of the residuals better than the separable Matérn model and improve the cross-validation results. The Spartan model provides the best cross validation measures among the three models. We also generate maps of groundwater level which illustrate the spatiotemporal variability in the basin. Παρουσιάστηκε στο: Spatial Statistics

Μη διαθέσιμη περίληψη Not available summarization Presented on

Δημοσίευση σε επιστημονικό περιοδικό Summarization: This work aims to present new modeling tools that help to better monitor and predict the groundwater level in sparsely gauged basins. The working area is the Mires basin of Mesara valley in the island of Crete (Greece). Efficient groundwater management in the basin is crucial in light of regional climate change model estimates showing a substantial risk of desertification for Crete. We propose that the prediction of the hydraulic head spatial variability in Mires basin can be improved by incorporating in the trend the distance of the prediction points from a temporary river crossing the basin and a component based on the generalized Thiem's equation for multiple wells as well as using the flexible Spartan semivariogram family to perform Residual Kriging. Our proposal is supported by the results of cross validation analysis. Our results are applicable to other unconfined aquifers. Παρουσιάστηκε στο: Advances in Water Resources