Irrigated agriculture consumes vast amounts of water and energy, yet it is key to high crop yields. This is especially important as we are facing increasing population and high variability in precipitation related to climate change. However, groundwater storage continues to be overdrawn due to irrigation withdrawals at unsustainable rates. We have been quantifying the effects of irrigated agriculture, management practices, and climate across the High Plains Aquifer, California’s Central Valley, and Michigan in the United States. Our team uses agricultural and integrated hydrologic models to characterize how management changes affect water resources and crop productivity. We map annual changes in irrigated agriculture and solar panel areas by integrating biophysical data and remote sensing imagery using machine learning. We then use this information as input to model the effects of agricultural management strategies on water use, crop yield, energy use, and water supplies. In a portion of the High Plains Aquifer, we showed the effects of two very different irrigation adaptation strategies on irrigation water use. This analysis showed that a local management solution, where groups of regional farmers collectively agreed to reduce their pumping, was a much more effective solution than a technology-based approach where more efficient irrigation technology was adopted that uses less water per acre. This research shows how the integration of remotely-sensed and ground-based data into fully-distributed integrated hydrologic models can help stakeholders move toward more sustainable agricultural practices. 

How to cite: Hyndman, D., Kendall, A., Partridge, T., Stid, J., and Zwickle, A.: Using Remote Sensing and Integrated Hydrologic Models to Characterize the How Irrigated Agriculture Affects Highly Overdrawn Aquifers in the United States, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17218, https://doi.org/10.5194/egusphere-egu23-17218, 2023.

Groundwater is a strategic reserve that is often used to meet water demands in dry seasons and during drought periods. However, the over-exploitation of this vital resources can jeopardize its sustainability. Projected climate change is expected to further exacerbate the situation in many regions of the world. Therefore, it is essential for decision makers to have simple tools to model groundwater flow and to assist in aquifer management. These tools can reduce the computational cost of complex physics-based models, without undermining the reliability of the results. The aim of this work is to develop a surrogate model capable of simulating groundwater flow in the Konya closed basin, a major agricultural region located in central Turkey. The model is used to analyze different future water demand scenarios and evaluate the possible effects of climate change and agricultural policies on groundwater. This aquifer is one of the pilot sites investigated within the “Innovative and Sustainable Groundwater Management In the Mediterranean (InTheMed)” project, which is part of the PRIMA programme. An Artificial Neural Network (ANN) was trained to provide groundwater levels at 30 monitoring points for the period 2020-2039 accounting for different climate and agricultural scenarios. The surrogate model replaces a full numerical surface-subsurface flow model implemented in MODFLOW and calibrated using field data recorded in the period 2000-2019. To define the dataset that feeds the ANN, two multiplicative coefficients were considered: one applied to the historical precipitation and the other to crop water demand. The two coefficients and the current month were considered as input features of the ANN, while the piezometric heads at the 30 monitoring points were the outputs. A dataset of 100 combinations of precipitation and crop coefficients was generated using the Latin Hypercube Sampling method, assuming an increase/decrease range in terms of precipitation equal to +/- 40% and water demand equal to +/- 25%. For each combination of the coefficients, the full numerical model was run starting from January 2020 to obtain piezometric heads at the 30 monitoring points with a monthly time discretization. The final dataset was used to train (70%), validate (15%) and test (15%) the network, highlighting a very good performance of the ANN for all three phases. The fully trained network was used to predict groundwater levels considering three different precipitation scenarios for the period 2020-2039: - 20% of the observed precipitation, no reduction of the observed precipitation and + 20% of the observed precipitation. For each precipitation scenario, the water demand was considered in the range -/+ 20%.

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.

How to cite: Secci, D., Todaro, V., Yologlu, O. C., Copty, N. K., Daloglu Çetinkaya, I., D'Oria, M., Saysel, A. K., Tanda, M. G., and Zanini, A.: An artificial neural network as a quick tool to assess the effects of climate change and agricultural policies on groundwater resources, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5801, https://doi.org/10.5194/egusphere-egu23-5801, 2023.

Land subsidence is one of most severe geohazards caused by excessive groundwater pumping which has gained increasingly interest over the last decades. Various numerical models have been developed to address this mechanism and support the groundwater policy-makers. In addition, the performance of these models has highlighted the importance of uncertainty quantification related to the unknown hydro-mechanical parameters. Bayesian inversion provides a powerful tool to statistically infer these uncertain parameters by incorporating the numerical model and the available observations. However, Bayesian scheme relies on the sampling algorithm to explore the parameter space. Such exploration requires enormous computational cost in which one realization of numerical model is already costly. Hence, parallel research focuses on surrogate models which aim to fast and accurately approximate the numerical solution with limited training realizations. In this study, Bayesian inversion is facilitated by substituting a coupled groundwater flow-geomechanical model with a surrogate model based on the sparse grid approach. More specifically, a 3D coupled variably-saturated groundwater flow-geomechanical model is first performed to describe the pressure head variation and deformation to the groundwater extraction from 1960 to 2012 in Alto Guadalentín Valley aquifer, Spain. Then, sparse grid method is used to construct the surrogate models which approximate the input/output mapping of the numerical simulator. Lastly, Monte Carlo Markov Chain yields the uncertainties of hydraulic conductivity and compressibility by assimilating the piezometric head records and displacement measurements obtained from Interferometric Synthetic Aperture Radar (InSAR) technique. Our preliminary results demonstrate that the surrogate model has high and fast performance on approximating the state variables in which misfits is negligible with respect to the measurement noise. Bayesian inversion can improve the characterization of parameters of interest whose posterior distributions are significantly constrained comparing to the prior distributions. Moreover, the numerical outcomes with calibrated parameters show a good fit with the available observations. In summary, the illustrated framework takes advantage of novel techniques from various aspects, including monitoring, numerical modeling, statistical analyses and provides a reliable and efficient way to infer properties of aquifer systems with ongoing water pressure depletion.

How to cite: Li, Y., Zoccarato, C., Tamellini, L., Piazzola, C., Ezquerro, P., Bru, G., Guardiola‐Albert, C., Bonì, R., and Teatini, P.: Surrogate-based Bayesian characterization of porous and deformable aquifer systems in water stressed regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8761, https://doi.org/10.5194/egusphere-egu23-8761, 2023.

Groundwater represents a strategic freshwater resource for multiple sectors, including drinking water, agriculture production, and ecosystem services. The Mediterranean Basin is a well-known water-scarce region that is increasingly relying on groundwater use, especially during drought periods. Many areas in the Mediterranean region are already facing water stress due to increasing demand and limited resources. Climate change is likely to exacerbate these issues, as it is expected to lead to more frequent and severe drought conditions in some areas as well as irregular rainfall in others. Due to the growing availability of data and computational processing capabilities nowadays, deep learning models are seeing an increase in popularity. In this study, we attempted to create 92 location-specific Convolutional Neural Network (CNN) models in wells spatially distributed over the Iberian Peninsula to estimate groundwater levels until the end of the century. Our models use monthly precipitation and temperature data as input variables. Specifically, we considered cumulative precipitation for 3, 6, 12, 18, 24, and 36 months to account for the recharge time lag between precipitation and groundwater changes. Once trained using historical precipitation and temperature records, the CNNs were applied to assess the influence of climate change on groundwater levels. For future climate projections, an ensemble of six combinations of distinct General Circulation Models (GCMs) and Regional Climate Models (RCMs) was considered under two Representative Concentration Pathways (RCPs): the RCP4.5 and RCP8.5. Our preliminary results revealed a more consistent decline in groundwater levels in the southwest region of the Iberian Peninsula under the RCP8.5 scenario, while a general more constant groundwater level under the RCP4.5 scenario has been detected towards the end of the century. Detailed results of this study will be shared and discussed during the event.

How to cite: Rouhani, A., D'Oria, M., Gómez-Hernández, J. J., Rode, M., and Jomaa, S.: Impact of climate change on groundwater levels in the Iberian Peninsula, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12968, https://doi.org/10.5194/egusphere-egu23-12968, 2023.

The recovery of aquifers under severe stress requires tools to understand their behaviour to define the most sustainable measures and the collaboration of all the stakeholders involved, water authorities, legislators, and groundwater users. The most used tools for decision-making are numerical models. However, numerical models are usually computationally expensive and due to their complexity are limited to people with advanced technical knowledge. This work proposes to replace the numerical model with a quick and simple-to-use tool accessible to all aquifer’s users. For that, we develop a surrogate model based on artificial intelligence methods. This groundwater flow surrogate model allows evaluating the impact of possible changes in pumping extractions or rainfall on piezometric heads in the near future. It has been applied to the Requena-Utiel and Cabrillas-Malacara aquifers, in Spain, considered overexploited by the Júcar River Basin Authority. The surrogate modelling methodology requires a numerical model to create the training data set for the machine learning algorithms. The numerical model is implemented and calibrated using MODFLOW 2005 and FloPy using all available information from 1980 to 2016, with a monthly discretization. The training dataset is obtained by generating 100 MODFLOW realizations with different scenarios of recharge and pumping rate and 145 selected monitoring points from 2016 to 2052. Recharge rate is allowed to decrease up to 60% or increase up to 25% of the average of the last ten years, while, pumping rates are allowed to decrease up to 70% and increase up to 30%. Then, the data is shuffled and 90% is used for training and the remaining 10% is used for testing. Three different algorithms were tried to compare the results: Random Forests, AdaBoost and XGBoost, and random forests were selected as the final surrogate model for its best performance. The surrogate models produce very similar and accurate approximations of the piezometric heads with respect to the data they were trained with and the reduction in computational time is remarkable. The predictions of the surrogate model are interpolated over the study area to obtain piezometric head values maps.

This research was developed under the scope of the InTheMED project, which is part of the PRIMA Program supported by the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No 1923.

How to cite: Uribe-Asarta, J., A. Godoy, V., and Gómez-Hernández, J. J.: Random Forests-Based Surrogate Model as a Tool to Facilitate Groundwater Management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13470, https://doi.org/10.5194/egusphere-egu23-13470, 2023.

The Nuapada region, which lies in the western margin of Odisha, India, has been suffering from severe drought conditions and critical aquifer-system stress for decades due to the continuous lowering of the water table, erratic rainfall patterns, and the existence of consolidated hard rock terrains of quartzite, gneisses over the region. Unsustainable groundwater management and continuous withdrawal of sub-surface water resources for domestic, agricultural, and industrial purposes bring depletion of the groundwater table a serious threat and drive interest in choosing the study area for the investigation purposes. 

This work has focused on locating the potential recharge zones in one of the drought-prone hilly tracts of western Odisha part, India, i.e., the northern blocks of Nuapada district, using GIS tools. Secondary datasets such as local administrative data, topomap, satellite data, forest cover and mineral data, population density, soil, precipitation and field information such as surface lithology, lineaments, structural trends, geomorphological features, drainage patterns as primary datasets were integrated into GIS platform to generate thematic maps such as geology, topography, contour, geomorphology, drainage, Lineaments, NDVI and Land Use Land Cover. Groundwater prospective zones over the study region were formed by integrating the thematic layers in the GIS platform. It’s been categorized into five zones such as excellent, good, moderate, moderate to poor, and poor. From the observations, it has been found that excellent and good zones account for around 8.428 and 374.906 Km² respectively of the total study area, whereas moderate, moderate to poor and poor zones account for approximately 734.77, 250.272, and 718.548 Km² respectively of the entire study area. The excellent, good, and moderate zones lie mainly in the northern and eastern parts of the study region. These areas are more suitable for attributing groundwater recharge structures such as check dams, percolation tanks, storage tanks, subsurface dyke, nalabund, contour bunding, and rooftop rainwater harvesting structures etc. The poor zones lie in the western half of the study region. Around half of Komna block and few patches of Nuapada block come under the poor zone category. These areas are unsuitable for building recharge structures. The poor zones are composed of hard rocky quartzite. The rest of the study region is covered with granite, granite gneiss, few patches of khondalites, and little alluvium. Accumulated residual, structural hills, steep sloped rugged topography, distribution of drainage pattern, land use pattern and confined to the semi-confined types of aquifers also play critical roles in the categorization of groundwater potential recharge zones. Inconsistent precipitation and climatic factors such as temperature, humidity and evapotranspiration lead to lowering of water table and acute drought conditions in the study region. The declining trend of the water table has been shown here to assess the amount of water resources in the subsurface region.

How to cite: Ojha, M.: Demarcation of lowered water table zones in a drought-affected area of western Odisha, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3742, https://doi.org/10.5194/egusphere-egu23-3742, 2023.

Climate change threats groundwater resources, requiring sustainable management of this critical asset. Widely applied in hydrogeology, direct current resistivity (DCR) methods have been a preferable tool for imaging groundwater resources due to its potential to efficiently image a relatively large area in a relatively short period. DCR methods use electrodes to inject electrical currents into the subsurface ad measure the resulting potential difference (i.e., voltage).

The quantitative interpretation of these data (i.e., the spatial prediction of the geological subsurface properties) requires solving a challenging geophysical inversion problem. Deterministic DCR inversion methods are common approaches to reach this objective but might be computationally expensive, requiring a large degree of expertise and predicting a single model unable to capture the small-scale details of subsurface geology. The main goal of this work is to overcome these limitations through the development and implementation of a deep DCR inversion workflow.

The proposed methodology follows three main steps: data acquisition, deep neural network (DNN) training and DCR data inversion. For the second step, it is generated a training dataset of electrical resistivity models by geostatistical simulation to represent a variety of possible subsurface scenarios. These models will be the input to train a variational autoencoder (VAE; Kingma & Welling, 2013; Lopez-Alvis et al., 2020). After training, the VAE outputs electrical resistivity simulated models given measured DCR data. The predicted models are then forward modelled (Cockett et al., 2015) to calculate predicted data, which are compared with the recorded data. The misfit between the observed and simulated data is used to iteratively update the DNN weights and parameters.

The proposed method is illustrated with its application to a set of DCR data acquired in the southern region of Portugal comprising an area highly affected by droughts and industrial pressure.

Cockett, R., Kang, S., Heagy, L. J., Pidlisecky, A., & Oldenburg, D. W. (2015). SimPEG: An open-source framework for simulation and gradient-based parameter estimation in geophysical applications. Computers and Geosciences, 85, 142–154. https://doi.org/10.1016/j.cageo.2015.09.015

Kingma, D. P., & Welling, M. (2013). Auto-Encoding Variational Bayes. https://doi.org/10.48550/arXiv.1312.6114

Lopez-Alvis, J., Laloy, E., Nguyen, F., & Hermans, T. (2020). Deep generative models in inversion: a review and development of a new approach based on a variational autoencoder. https://doi.org/10.1016/j.cageo.2021.104762

How to cite: Escada, C. and Azevedo, L.: Deep Direct Current Resistivity Inversion, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-299, https://doi.org/10.5194/egusphere-egu23-299, 2023.

Electrical resistivity tomography (ERT) is a geophysical method used to imaging the subsurface and widely used in hydrogeological studies due to its sensitivity to electrical resistivity, which is directly related to rock type, porosity, ionic strength of the pore fluids, and surface conductivity of geologic materials. The prediction of subsurface properties from the recorded data at the surface requires solving a challenging geophysical inversion problem. For near-surface characterization studies, this is often accomplished with deterministic electrical resistivity inversion methods. Deterministic geophysical inversion approaches linearize the problem around an initial solution, resulting in a single smooth representation of the subsurface. Deterministic models are unable to capture the natural variability of the subsurface. Moreover, a single solution does not yield enough information for accurate uncertainty assessment. Contrary to deterministic approaches, stochastic inversion methods predict multiple model realizations that fit similarly the recorded geophysical data and allow assessing uncertainties. Lately, deep learning algorithms based on deep generative models have been used to re-parametrize model and data spaces into low-dimensional domains and solve geophysical inverse problems in a more efficient way.

We propose an ERT inversion methodology in which a deep convolutional variational autoencoder (VAE) network is trained with a set of electrical resistivity models generated using geostatistical simulation. After training the VAE, the latent space is perturbed and updated iteratively with adaptive stochastic sampling to generate electrical resistivity models by inputting the optimized latent vectors to the decoder part of the VAE. From the set of decoded models, we use a finite volume approximation of Poisson’s equation to compute synthetic apparent resistivity models. The misfit between predicted and observed apparent resistivity data is used to drive the convergence of the iterative procedure and condition the optimization of new models in the subsequent iterations.

The proposed methodology is illustrated by applying it to both a two-dimensional synthetic case and to a two-dimensional profile obtained from an ERT survey carried out in an area located in the Southern region of Portugal. In both application examples, the predicted models generate synthetic geophysical data that match the observed one. We show the ability of the model to assess spatial uncertainty and compare the results obtained in the real data set against commercial deterministic ERT inversion methodology.

The work presented herein is supported by the PRIMA programme under grant agreement No. 1923, project Innovative and Sustainable Groundwater Management in the Mediterranean (InTheMED). The PRIMA programme is supported by the European Union.

How to cite: Azevedo, L. and L. Pereira, J.: Deep generative inversion of ERT data for electrical resistivity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15753, https://doi.org/10.5194/egusphere-egu23-15753, 2023.

Groundwater sources in arid and semi-arid regions are expected to be gradually more stressed due to multiple causes, such as the depletion of surface water resources, water quality degradation, increasing demand of agriculture and economic and demographic growth.   As a result, there is a need to secure the availability and quality of groundwater reserves in those areas.

Large and high-dimensional datasets are required to fully characterize the physico-chemical properties of the complex groundwater processes. Subsequently, statistical tools are used to provide estimations of these properties beyond the sampling locations, which are required in order to provide reliable assessments of the associated risks. Geostatistical tools are widely used in groundwater applications to estimate the spatial variability of quality parameters by combining different types of datasets to build local models of spatial uncertainty. Special attention is given to the kriging method, which provides an estimate of the unknown quality variable value along with a measure of uncertainty regarding that estimate.

In the current study, groundwater samples are collected from an unconfined aquifer, located at north-eastern Grombalia (Tunisia). Ordinary kriging is used to estimate the spatial variability of piezometric levels and quality parameters. Sampling data are subjected to suitable transformations prior geostatistical computations so that the Gaussian assumption is satisfied, whereas the results are back-transformed to the original space. Different numbers of neighboring data points are considered to decide the spatial extent of the search neighborhood by comparing cross-validation errors. In addition, indicator kriging is used to construct probability maps of the quality parameters, and identify regions that possess high probability to exceed irrigation water quality standards.

Acknowledgement

This work is conducted under ‘EXCELSIOR’ project (www.excelsior2020.eu), which has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology. This work is also supported by the PRIMA program under grant agreement No1923, project InTheMED. The PRIMA program is supported by the European Union.

How to cite: Panagiotou, C. F., Akrout, H., Baccouche, H., Mellah, T., Mansouri, L., and Ghrabi, A.: Assessment of groundwater quality and piezometric levels using geostatistical methods in Grombalia aquifer, Tunisia., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8037, https://doi.org/10.5194/egusphere-egu23-8037, 2023.

The High Plains aquifer in the central United States is one of the world’s largest and most important regional aquifers in terms of the agricultural production that its waters support. The portion of the aquifer in the state of Kansas has been heavily stressed for decades, producing large water-level declines that have called into question the continued viability of groundwater-based irrigated agriculture and the rural communities that depend on it. Given the sparsity of surface water in the region, reductions in pumping, which are typically accompanied by modifications of agricultural practices, are often the only option to extend the aquifer lifespan. Such reductions, however, must be implemented over a relatively large area to make a significant impact on regional decline rates. In 2012, the Kansas Legislature approved a new groundwater management option to facilitate pumping reductions, the Local Enhanced Management Area (LEMA) program. This program allows the development of locally generated management plans that are then supported by regulatory oversight. The first LEMA, the Sheridan-6 (SD-6) LEMA, was established in 2013 in a 255 square kilometer area in northwest Kansas with the goal of reducing water use by 20% relative to the prior average use. In the first decade, the pumping reduction was close to 30% after controlling for climatic conditions. More importantly, the water-level decline rate decreased by over 50%, thereby extending the aquifer lifespan by over five years during the first ten years of the LEMA. The ultimate extension of the aquifer lifespan, which will likely be much greater, depends on how net inflow changes with time. Until now, net inflow has remained close to the pre-reduction level. Eventually, however, it will decrease in response to the pumping reductions. Continued monitoring will enable the timing and magnitude of that decrease to be quantified. The success of the SD-6 LEMA has led to the establishment of larger LEMAs in 2018 (12,623 square kilometers) and 2021 (663 square kilometers) with an additional LEMA under consideration. If the success of the irrigators in the SD-6 LEMA can be duplicated in these larger areas, the lifespan of the High Plains aquifer in Kansas will be significantly extended.

How to cite: Butler, J.: Extending aquifer lifespans with pumping reductions: Experiences from the High Plains aquifer, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9552, https://doi.org/10.5194/egusphere-egu23-9552, 2023.

Managed aquifer recharge (MAR) is a powerful approach to counteracting the negative impacts of overexploited groundwater resources and enhancing groundwater availability in water-scarce regions. One common MAR strategy is aquifer storage and recovery (ASR). Theoretically, the ASR techniques can be adopted for karst systems to store and recover water within the system or to increase the subsurface inflow into the alluvial system from the adjacent upland karst system. However, karst systems present a particular challenge for the application and technical implementation of ASR, due to the associated strong anisotropy and heterogeneity, high runoff dynamics, as well as the underlying uncertainties regarding the available data.

This research is oriented to provide a regional scale model of a karst-alluvial system in Lebanon, analyzing the impact of MAR scenarios as well as quantifying boundary inflow to adjacent alluvial systems. For this reason, several model conceptualizations (considering multi-model concepts) and ASR application scenarios are adopted and tested. For transferability, real-world case studies and idealized yet generalizable systems are considered and assessed. The model pre- and postprocessing is entirely script-based and uses open-source tools to ensure sustainable use

An anisotropic fast-marching algorithm is used to create spatially distributed karst channel networks through the Python package pyKasso. In addition, a discrete continuum model is developed (e.g., CfPy), where the stochastic conduit networks are implemented as a large ensemble of the plausible and representative karstic system. The regional karst alluvial model results are also used to make general recommendations for MAR site selection in the karstic study site.

How to cite: Genzel, M., Reimann, T., Kavousi Heydari, A., Hartmann, A., Doummar, J., and Rudolph, M.-G.: Regional-scale groundwater modeling, focusing on boundary inflow and impact assessment of managed aquifer recharge in karst systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16393, https://doi.org/10.5194/egusphere-egu23-16393, 2023.

The tendency towards more groundwater use is becoming increasingly prevalent in regions where surface water supplies are limited in quantity and quality. However, the over-abstraction of groundwater can lead to serious issues. Therefore, to maintain the supply-demand balance for groundwater, withdrawals from aquifers should not exceed groundwater recharge. In cases where they do, groundwater reserves should be replenished with external water. Returning treated wastewater to the aquifer via managed aquifer recharge structures is a proven strategy that can effectively increase groundwater recharge rates. The objective of this study is to use groundwater flow modeling to assess the impacts of recharging treated wastewater to the aquifer at the sub-basin scale. The study is realized within the scope of the European Union-supported research project TRUST, which is conducted by six Mediterranean countries to mitigate water scarcity under the influence of climate change.

A MODFLOW-based groundwater flow model is constructed for the Fetrek Creek sub-basin, which is located in an environmentally stressed semi-urban area in Western Turkiye. The input data for the model consists of information obtained from reviewing and synthesizing borehole logs, groundwater recharge rates from an independent hydrological modeling study, groundwater level time series from 17 monitoring wells, and information from previous geological and hydrogeological studies. The PEST parameter optimization method is used to calibrate the model. First, a baseline scenario representing the current state is investigated using the calibrated flow model. The model is then used to determine the impacts of various managed aquifer recharge application scenarios, thereby reflecting wastewater reuse in the sub-basin.

Acknowledgment:

This study is funded by the PRIMA program supported by the European Union under grant agreement No: 2024, project TRUST (management of industrial Treated wastewater ReUse as mitigation measures to water Scarcity in climaTe change context in two Mediterranean regions).

How to cite: Eryilmaz Saylan, C., Elci, A., and Somay Altas, M.: A Groundwater Flow Modeling Application for the Impact Assessment of Treated Wastewater Reuse by Managed Aquifer Recharge, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13121, https://doi.org/10.5194/egusphere-egu23-13121, 2023.

An integrated perspective of the terrestrial part of the hydrological cycle from precipitation over soil and aquifer storage to river discharge is fundamental for a sound hydro(geo)logical understanding and, hence, a sustainable groundwater management. Simplified (or reduced-order) approaches can be an efficient tool to focus on the most relevant drivers, processes and responses of a system based on a parsimonious set of parameters and a relatively simple computational implementation. The Urucuia sandstone aquifer system in north-eastern Brazil is a high-plain region with mostly deep unconfined water tables and intensive groundwater pumping for agricultural irrigation causing social and political conflict. Here we develop a coupled reduced-order model of the high-plain and valley aquifer portions in the southern part (approximately 10000 km²) of the Urucuia aquifer, considering the soil zone (partitioning rainfall into evapotranspiration and deep percolation), thick vadose zones (up to approximately 100 m deep with unsaturated vertical moisture transport), and the saturated zone (generally over 100 m thick) providing river discharge. Data for model input (precipitation and potential evapotranspiration) and validation (river discharge) are obtained from the CAMELS-BR database as time series over approximately 40 years. Additional but shorter time series of groundwater levels in high-plain and valley regions are obtained from the RIMAS well database. We show that the much lower seasonality in water table fluctuations observed in the high-plain regions can be explained by the deeper unsaturated zone. While seasonality in river discharge may be attributed to the base flow from the valley aquifer portion, discharge recessions during the dry (practically zero rainfall) months of the year are sustained by the much larger aquifer portions underlying the high-plains. Groundwater pumping is considered as abstraction from storage in the saturated zone and its impact on groundwater levels and river discharges are evaluated with respect to climatic oscillations and long-term trends. The fact that the unsaturated moisture transport through the thick vadose zones under the extensive high-plains may take several years offers an interesting opportunity (in term of lead time) for groundwater management, if deep percolation leaving the soil zone is adequately estimated or measured over time.

How to cite: Klammler, H., Araújo da Silva Leão, C., Bastos Leal, L. R., and Hatfield, K.: Simplified integrated modeling and potential groundwater management benefits of the unsaturated zone in the Urucuia aquifer system, Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4229, https://doi.org/10.5194/egusphere-egu23-4229, 2023.

Groundwater storage potential is defined as the total amount of permanent storage that exists in the aquifers. The significance of groundwater storage potential is effectively concerned with climate-related impacts on groundwater resources. The current demand for groundwater for societal needs will hasten the depletion rate of the regional groundwater potential. The present research employs a groundwater modelling tool called MODFLOW to estimate the regional groundwater sensitivity, potential and conceptual simulations of groundwater flow in the study region. The groundwater levels of the Solapur region from 2010 to 2021 were used to investigate the region's groundwater potential, along with other parameters, viz., aquifer type and hydraulic conductivity in both vertical and horizontal directions. The groundwater level shows a sensitive response to the rainfall intensity, but the unconsolidated materials have greater water depths regardless of the season. Consolidated material formations show lower available water depths, irrespective of the season. The simulated results of the study show that the regional groundwater depths are sensitive to hydraulic conductivity in both horizontal and vertical directions. The zone budget's findings shows that the regional inflow and outflow are nearly equal. The study replicates that the Solapur area is semi-arid. As a consequence, this research is relevant to all semi-arid places where groundwater is becoming the dominant source of social requirements. Such a study would be useful for appropriate planning and management of groundwater for future requirements.

Keywords:

Groundwater level, Rainfall, Geology, Modflow, Zone budget.

How to cite: Patil, Y. and Landage, A.: Evaluation of the Semi-Arid Region Groundwater Storage Potential for Solapur Region, Maharashtra, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4199, https://doi.org/10.5194/egusphere-egu23-4199, 2023.

Springs are a critical water source for the people of the Indian Himalayas, sustaining their domestic and agricultural requirements. However, spring discharges are declining and flow regimes have transitioned into ephemeral or intermittent systems, primarily due to rainfall variability, landuse transformations and anthropogenic actions. Consequently, water scarcity has become a major challenge. To ensure water and livelihood security, quantifying spring water responses and understanding aquifer recharge is paramount for maintaining spring ecosystem services. Experiments were conducted to enable site-evidence-based intervention designs for spring rejuvenation through a robust management framework. In this study, we operationalize pilot observatories in the Uttarakhand Himalayas, India and integrate the application of hydrological time-series analysis, stable isotopes and water chemistry to understand spring watershed behaviour. We instrumented springs (A_1, P₁, P₂, P₃) for high-resolution hydrological monitoring. Spring hydrodynamics was assessed by employing Hydrograph and flow recession analysis. Autocorrelation and cross-correlation functions were used to estimate the system memory and reveal rainfall and spring interdependence. Isotopes and water quality were sampled bi-monthly at selected springs/streams (S₁-S₁₇, St₁-St₅) since December 2021. Through isotope analysis and reflecting on the geochemical evolution of springwater along flow paths, attempts were made to understand the origin (recharge) of water types.  

Inferences show that spring A₁ indicates intricate flow networks and slow flow velocities, while P₁, P₂, and P₃ springs are characteristics of transmissive fractures. A low value of recession coefficient ‘α’ for A₁ depicts diffused fracture system compared to P₁, P₂, and P_3, which indicates rapid aquifer emptying and a well-interconnected flow network. Correllograms for A₁ decline (r_xx(k) value) steadily show a high memory of 120 days, while P₁, P₂, P₃ exhibit shorter system memory and poor drainage flow network. A better storage capacity and homogeneity of underlying geology for A₁ are revealed compared to P₁, P₂, and P₃. Isotope values range from -8.1‰ (S₁₂) indicating anthropogenic forcing at recharge zones to -9.7‰ (S₆), representative of natural recharge conditions. The characteristic δ¹⁸O-δD regression line has shallower slopes than Global Meteoric Water Line, indicative of multiple moisture source mixing. S₆ is monitored for intervention planning and shows isotopic values distinctive of high elevations and far transport of water-bearing clouds. Two hydrochemical facies HCO₃-Ca and mixed HCO₃-Ca-Mg, were determined from the Piper ternary diagram which indicates carbonate rock geology and flow evolution through pathways.

The research aims to improve the understanding of mountain hydrological processes and drivers of groundwater fluxes. Such an integrated-approach permits detailed process understanding and limits erroneous interpretations. Policymakers can extend the results across the Indian Himalayas to inform management decisions and frameworks. 

Keywords: Springshed hydrodynamics, systems memory, long-term observatory, spring aquifer, Himalayas

How to cite: Dass, B. and Sen, S.: Understanding spring aquifer dynamics through observational data patterns, stable isotopes, and hydrochemistry – An account of experimental pilots in the Indian Himalayas., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-702, https://doi.org/10.5194/egusphere-egu23-702, 2023.

Groundwater-fed springs are relatively stable freshwater environments which harbor a valuable biodiversity and represent a precious water source for human needs. However, with ongoing global warming and increasing human footprint on natural systems, spring ecosystems are more and more exposed to hydrologic and physico-chemical alterations, which would in turn threaten their biodiversity. Recently, scientists claimed that an integrated “ecohydrogeological” approach, analyzing patterns and drivers of changes in spring environments from various perspectives, would greatly help in assuring the long-term preservation of these precious ecosystems. Following this plea, we studied both the ecological and the hydrological dynamics of the Presciano karst spring system (Abruzzo, Central Italy). The Crustacea Copepoda were chosen as target invertebrate group, since they are main components of the freshwater meiofauna and occur in all the groundwater-dependent ecosystems, groundwater-fed springs included. Specifically, we analyzed temporal changes in both copepod species richness and between-site differences in assemblage composition (i.e., beta diversity). The observed patterns were also contrasted to variations in measured physico-chemical parameters. On the hydrologic side, starting from rain gauge data, we implemented the CETEMPS Hydrological Model (CHyM) to derive a rain field with hourly resolution across the Gran Sasso – Sirente hydrogeological basin, which hosts the recharge areas feeding the Presciano spring. The modelled monthly cumulative rain, averaged across the Gran Sasso – Sirente basin, was then compared to the maximum and minimum monthly flow discharge values extracted from a hydrometric station located downstream the Presciano spring outlet. Finally, future rainfall simulations for the 2081-2091 decade were performed on the same area by forcing the CHyM software under the RCP8.5 emission trajectory, based on three different Regional Climate Models. Both species richness and beta diversity of the Presciano spring noticeably varied over time but variability of hydrochemistry could not comprehensively explain such changes, except for species turnover which was tightly related to between-site variability in water oxygenation. Seasonal discharge variations may thus have a more prominent role than local water conditions in determining the overall structure of spring meiofauna assemblages. Moreover, a not-negligible signal emerged from the comparison of precipitation and discharge temporal dynamics, indicating that accurately modelling rainfall in recharge areas would permit to better estimate and possibly forecast temporal variation in spring discharge. The future projections highlighted an overall predicted drop in precipitation across the Gran Sasso − Sirente basin, warning about possible groundwater lowering in karst springs in the next future.

How to cite: Lombardi, A., Cerasoli, F., Tomassetti, B., Tuccella, P., Redaelli, G., Di Cicco, M., Fiasca, B., Di Sabatino, A., and Galassi, D. M. P.: Preliminary assessment of the relationships between rainfall, flow discharge and biodiversity changes in a karst spring system in central Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12755, https://doi.org/10.5194/egusphere-egu23-12755, 2023.

Managed Aquifer Recharge (MAR) with desalinated saline water is of increasing importance to mitigate groundwater overexploitation and improving its quality. Besides high energy demand, fully desalinated water needs to be post-treated to increase the total dissolved solids concentration for its further use. As a new approach, the aim of the cooperative project “innovatION” is the development of a monovalent-selective membrane capacitive deionization method to improve the ecological footprint and to deliver a purposeful removal of ions. Nonetheless, the infiltration of a water with different water chemistry than natural pore- or groundwater causes geochemical interactions between water and sediment. 

Here, we present first insights of geochemical water-sediment interactions during infiltration of a monovalent partial desalinated water (mPDW) into three different dune sediments from the barrier island Langeoog, Northern Germany, by conducted column experiments. The island of Langeoog was chosen as one of the demonstration sites of the project. The results of the column experiments show that ongoing processes such as cation exchange and calcite dissolution depend clearly on the sediment characteristics. The more pedogenically developed the infiltrating media is, the more complex the geochemical interactions get. Calcite dissolution takes place during infiltration into beach sediment with a higher carbonate content, whereas infiltration into decalcified brown dune sands shows accumulation/adsorption of Ca²⁺. Grey dune sands appear to be a suitable location for a potential MAR application on Langeoog due to less distinct geochemical reactions. Numerical investigation of the respective experiments is shown in a companion study by Schloo et al. (submitted to EGU2023). Trace element mobilization was shown to not just depend on shifting redox conditions but also on the chemical composition of the infiltrating water potentially linked to colloidal transport. Especially, As and V mobilization were periodically retained during mPDW infiltration. Nevertheless, all reactions are shown to be time limited during the experiments and unlikely to cause major problems, hence MAR with mPDW on Langeoog might be a suitable approach to secure the freshwater lens volume in future in an energy efficient way.

How to cite: Braeunig, L., Schloo, M., Burke, V., Greskowiak, J., and Massmann, G.: Geochemical side effects of potential Managed Aquifer Recharge during infiltration of monovalent partial desalinated water into different dune sands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15634, https://doi.org/10.5194/egusphere-egu23-15634, 2023.

The Water Resources Information System for Germany (WIS-D) project aims to significantly improve the decision-making basis for actors in water management. The target group are expert users at water suppliers, authorities or institutions of the federal or state governments as well as the institutions of public enforcement (e.g. in the allocation of water rights). The aim is to support the current water balance management as well as the adaptation to climate change.

WIS-D, startet in 2021, consists of two interacting platforms. The first platform (dialogue platform) ensures the professional exchange between practice partners and research and the co-production of knowledge. Here, in the circle of the stakeholder cooperation group with eight external institutions, the goals and steps defined in the project application were first discussed bi-laterally and, in a second step, in a workshop, and the stakeholder needs were recorded. For this purpose, information on required water balance variables, indicators and time scales was also requested. Stakeholder interest in climate-hydrology simulations was very high, e.g. for the allocation of water rights. The possibility of using the resulting data sets for WIS-D was contractually agreed. Additional stakeholder requirements, such as uncertainty information, were collected in the stakeholder process.

The requirements and functionalities of the second platform (technical platform) were also defined in a co-production process by the stakeholders and scientists. Based on this, the first experimental prototype was developed and made publicly available at https://webapp.ufz.de/wis-d/ in August 2022. As a change to the original schedule, the stakeholders prioritized, for example, the possibility of regional evaluability in the online portal, so that this functionality, planned for the third project year, has already been integrated into the technical platform today. WIS-D is on its way to providing an uniform database on water balance components for past, present and future across Germany. This can be used to assess the trustworthiness of existing data sets. In addition, WIS-D can help to fill water management related data and information gaps.

The contribution describes the stakeholder process, shows the functionalities of the technical platform, and discusses the challenges of WIS-D development.

How to cite: Andreas, M., Friedrich, B., Christian, S., Rebekka, L., Thomas, S., Jan, B., Georg, T., and Sabine, A.: Development of a national-scale decision support system for the water sector in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7678, https://doi.org/10.5194/egusphere-egu23-7678, 2023.

We develop a high order, multi-loop nonlinear (system dynamics) model for socio-economic sustainability assessment of groundwater resources. Structure and behavior validation of dynamic system models as such pose several challenges. While some of these challenges stem from the ambiguities in theoretical representations of multi-sectorial systems, others stem from the lack of sufficient time varying data sets to calibrate the model reference behavior. In this paper, we focus on partial validation of the groundwater component of this multi-sectorial model. For this purpose, we test the behavioral response of the system dynamics model against a process-based surface/subsurface hydrogeological model. For testing purposes, the hydrogeological model is regarded as the best representation of the reality (a synthetic reality), concerning groundwater flows and accumulations. The system dynamics model is built on Stella Architect and runs on annual time steps for a time horizon of 20 years. The embedded groundwater is a non-spatial, one compartment representation -a box model- of the saturated zone, changing with lateral flows, vertical recharge and anthropogenic extractions. The hydrogeological model is built on the UFZ1-MODFLOW computer program. It simulates evapotranspiration and one-dimensional vertical flow through the vadose zone, and horizontal flow through the underlying aquifer system. The model was developed based on available borehole and pumping tests data and calibrated using observed transient groundwater level data. While the box model and the hydrogeological model are different structural entities, the spatially aggregated behavioral response of the latter under specific experimental conditions help structural validation and calibration of the box model. For this purpose, we apply multiple tests on the lateral and vertical recharge of the hydrogeological model under constant boundary hydraulic head, precipitation, irrigation and evapotranspiration conditions to observe the response in spatially aggregated hydraulic head. We repeat the same experiments on the box model, first to confirm the equations used to simulate the aggregated hydraulic flows, and to estimate the two parameters, “aquifer bottom” and “fractional evaporation” for calibration of the spatially aggregated hydraulic head. The testing process is very useful to arrive at a reliable water budget and hydraulic head response in the system dynamics model, which is going to serve for socio-economic sustainability analysis under stakeholder participation.

This work was developed under the scope of the InTheMED project. InTheMED is part of the PRIMA program supported by the European Union’s HORIZON 2020 research and innovation program under grant agreement No 1923.

How to cite: Uygur, İ., Yoloğlu, O. C., Copty, N. K., Daloğlu Çetinkaya, İ., and Saysel, A. K.: Partial validation of a socio-economic system dynamics model against a process based hydro-geological model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3417, https://doi.org/10.5194/egusphere-egu23-3417, 2023.

The Konya Closed Basin (KCB) in central Turkey is a vast fertile plain that is one of the major agricultural regions of the country and critical to its food supply and security. KCB is characterized by a semi-arid climate, with mean annual precipitation of 379 mm. Since most of the rain occurs in the winter months outside the agricultural season, groundwater is used extensively for irrigation. In recent years, groundwater levels have experienced sharp declines due to the expansion of irrigated lands and the switch to more water-demanding crops. This problem is exacerbated by a large number of unregulated groundwater extraction wells. A regional numerical surface-subsurface water flow model based on the UZF1-MODFLOW computer program was developed for the science-based management of this vital resource. The model simulates transient vertical flow through the vadose zone and groundwater flow through the underlying aquifer system. Because of the lack of direct measurements of groundwater abstraction rates, the time-dependent monthly rate of groundwater abstraction was estimated indirectly based on crop water requirements and historical land allocation for the different crops. The hydrogeology of the site was characterized from borehole data and conducted pumping tests. Historical groundwater level observations between the years 2000-2020 were used to calibrate the model. The key calibration parameter was irrigation efficiency. The challenges of developing and calibrating a regional water flow model are discussed. The calibrated model was then used to simulate the impact of different groundwater conservation scenarios: a slow and a fast transition to less water-demanding crops and the adoption of an optimized cropping pattern. The scenarios are evaluated against the business-as-usual scenario that assumes historical water demand trends remain unchanged. The results underline the urgent need to consider a holistic approach to address the water deficit of the basin. Overall, the model can help policy and decision-makers explore more efficient and sustainable groundwater management practices.

This work was developed under the scope of the InTheMED project. InTheMED is part of the PRIMA program supported by the European Union’s HORIZON 2020 research and innovation program under grant agreement No 1923.

How to cite: Yoloğlu, O. C., Uygur, İ., Copty, N. K., Daloğlu Çetinkaya, İ., and Saysel, A. K.: Evaluation of Different Water Management Practices for the Sustainable Use of Groundwater Resources in the Konya Closed Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8796, https://doi.org/10.5194/egusphere-egu23-8796, 2023.

The eGROUNDWATER project aims to improve sustainable, participatory groundwater management in the Mediterranean region by developing and testing Enhanced Information Systems (EIS) that integrate citizen science and information and communication technology (ICT) tools. A key component of the EIS is a mobile app that will allow farmers and other groundwater users to report groundwater levels in their wells and the amount of water used for irrigation and other purposes. At the same time, the app will provide users with information about the state of the aquifer and recommendations for sustainable water use (for example, short-term and seasonal predictions of irrigation water needs).

The eGROUNDWATER app has the potential to be a valuable tool for improving the sustainable use and management of aquifers in the Mediterranean region. By gathering real-time data from a wide range of users, the app will help to create a more complete and accurate picture of groundwater conditions and usage. Policymakers and resource managers can use this information to make informed decisions about the allocation and use of water resources. It can also help to identify potential problems and areas where conservation efforts may be needed.

The development of an app that meets the needs of the users required first to understand their perspectives and experiences related to the groundwater body. eGROUNDWATER has organized interviews and meetings in each case study to characterize the vision of the different agents on the groundwater bodies, and to try to build a collective framing of the current groundwater status and use in the area. On these meetings, the users identified lack of information about the aquifer as a critical issue to solve in order to advance towards a sustainable management of the resource.

In addition to providing valuable data and information, the eGROUNDWATER app has the potential to engage and educate farmers and other users about the importance of sustainable water management. The app can foster a sense of ownership and responsibility for the aquifer's health among users, as it offers personalized feedback and information about the use of water and the aquifer’s health. This, in turn, could lead to more responsible water use practices and help to preserve groundwater resources for the long-term.

The eGROUNDWATER project and its accompanying mobile app offer a promising approach to improving the sustainable use and management of aquifers in the Mediterranean region. The engagement of stakeholders in the development process and the collection and sharing of useful data and information through the app can help promoting education and awareness about water resource management. These efforts can help to foster a greater understanding of the importance of these resources and encourage more sustainable use of aquifers in the region, significantly contributing to the long-term sustainability of Mediterranean aquifers.

Acknowledgements:

This study has received funding from the eGROUNDWATER project (GA n. 1921) a project from the PRIMA programme, supported by Horizon 2020, the European Union's Framework Programme for Research and Innovation.

How to cite: Rubio-Martin, A., Pulido-Velazquez, M., Lopez-Perez, E., Sanchis-Ibor, C., Garcia-Prats, A., Manzano-Juarez, J., Garcia-Molla, M., Jimenez-Bello, M. A., Peñalvo-Lopez, E., Rinaudo, J.-D., Faysse, N., Nieto-Romero, M., Bento, S., Nunes, L., Serrao de Sousa, V., Bouzidi, Z., and Nejjari, A.: IMPROVING GROUNDWATER MANAGEMENT IN THE MEDITERRANEAN REGION THROUGH CITIZEN SCIENCE AND TECHNOLOGY: THE eGROUNDWATER PROJECT, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11715, https://doi.org/10.5194/egusphere-egu23-11715, 2023.

The Mediterranean region has been facing human and climate–change threats in the last decades. The current work, within the InTheMED PRIMA project, implements a groundwater simulation model in combination with optimization techniques and aims to elaborate informed alternatives in the decision–making process for the Tympaki study area.  Tympaki is located in the south of Crete and represents one of the most productive areas in the agricultural sector of the island. The economic activities combined with its location make it a very demanding region in terms of water needs. Most of this demand is met from the coastal aquifer and, more recently, from a reservoir. Climate change, the increase in temperatures and decrease in precipitation, cannot guarantee water resources of sufficient quantity and/or quality. To address these issues, an optimization analysis was conducted. The optimization problem was to maximize the pumping rates subject to a water table that improves or maintains the situation in the aquifer. In the case of Tympaki, this also helps solve the problem of saltwater intrusion. FEFLOW was used to simulate groundwater flow in the porous media and MATLAB application was used for optimization. Since groundwater flow is not a linear process, iterative simulation–optimization runs were performed in the framework of piecewise linear optimization. To avoid time–consuming procedures, customized GUIs were developed for better data processing in MATLAB. The simulation–optimization model was applied to recorded measurements and different time periods, as well as to regional climate model data. In addition, different water management scenarios were evaluated including alternative water sources, such as water from the reservoir or treated wastewater. The results are presented in online maps so that they can be disseminated among stakeholders and help inform all interested parties and enable more transparent decision–making.

This work was developed under the scope of the InTheMED and Sustain-COAST projects.

InTheMED is part of the PRIMA programme supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 1923. Sustain-COAST is funded by the General Secretariat for Research and Innovation 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.

How to cite: Anyfanti, I. V., Lyronis, A., Diakoparaskevas, P., Varouchakis, E., and Karatzas, G. P.: Sustainable groundwater management using a combined simulation–optimization approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5943, https://doi.org/10.5194/egusphere-egu23-5943, 2023.

Human activities place additional pressure on the limited water resources in arid and semi-arid areas, and the overexploitation of groundwater threatens its quality and sustainability. Non-conventional water represents an alternative and extra resource in this situation. Reuse and recycling the treated wastewater reduce the pollution and protect the groundwater resources against pollution and reinforce their sustainability. Textile industry produce and release wastewater near rivers in the Grombalia case study site (Tunisia), which is characterized by high levels of organic, inorganic materials and dyes residues. Innovative scenarios based on the anodic oxidation of textile effluents are developed in the current study. The findings of treated wastewater quality, treatment costs, and related environmental impacts are contrasted with those of in situ treatment (a reference scenario). The effectiveness of electrochemical technology is related to the simultaneous oxidation and reduction on bipolar BDD electrodes. Three optimized treatment scenarios are proposed to improve the real-world applicability of the sustainable treatment. The best solution is the anodic oxidation post-treatment which was selected based on pollution removal and economic costs. Eco-efficiency results confirmed this choice in terms of environmental benefits which are quantified by COD removal enhancement and water reuse potential.

Keywords: groundwater pollution, textile industry, anodic oxidation, eco efficiency, Reuse.

Acknowledgment

“This paper is supported by the PRIMA program under grant agreement No1923, project InTheMED.. The PRIMA program is supported by the European Union”.

How to cite: Akrout, H., Mellah, T., Mansouri, L., Baccouche, H., and Ghrabi, A.: Assessment of sustainable textile Wastewater Treatment for providing of non-conventional Water- resource related to other activities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3966, https://doi.org/10.5194/egusphere-egu23-3966, 2023.

Climate change scenarios obtained by models foresee changes in the availability of water resources that make it necessary to improve their management, especially for groundwater, as they are one of the most important, but at the same time most vulnerable, sources of fresh water on the planet. Groundwater governance can be defined as "the process by which groundwater is managed through the application of elements such as accountability, participation, availability of information, transparency, customs and policy." The manner in which this governance is carried out can have direct implications for the status of groundwater, both qualitatively and quantitatively, hence its importance when analysing the status of an aquifer or groundwater body. The objective of this contribution is to present GTool, a brand new digital and user-driven tool developed in the framework of the EU PRIMA project GOTHAM, which aims at establishing a new groundwater governance model based on a bottom-up approach. One of the modules that GTool integrates, called “Groundwater Response Module”, has been designed with the objective of forecasting the most probable groundwater body status according to a selection of variables. The input data used to assess the current and expected status of the aquifer comprises groundwater quality trends, prediction of scarcity and drought indexes, the potential use of non-conventional water resources (reused water), the increase of available resources by the implementation of managed aquifer recharge (MAR) techniques, potential groundwater quality restoration by means of remediation techniques, and current groundwater governance. This methodology has been applied in Campo de Dalías-Sierra de Gádor groundwater body (Southern Spain), which is characterised by large groundwater abstraction (mainly for the irrigation of greenhouses crops), steep and lasting drops of groundwater levels and subsequent groundwater quality degradation by seawater intrusion and salinity increase. Results show that, despite the good groundwater governance and high MAR and non-conventional water resources potential currently existing in the area, Campo de Dalías-Sierra de Gádor groundwater body is expected to have a ‘bad’ status in the medium term, principally due to the forecasted scarcity and drought indexes and, secondarily, upward trends regarding salinity (electrical conductivity, chloride) and nitrate contents.

How to cite: Sánchez García, D., Argamasilla Ruiz, M., Palomino Gómez, A., Díaz Hurtado, M. Á., García Ortiz, L., Núñez Moreno, F., Nieto López, J. M., and Aguilera Romero, D.: GTool: A new user-driven tool aiming at achieving a sustainable groundwater governance in Mediterranean aquifers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16409, https://doi.org/10.5194/egusphere-egu23-16409, 2023.