The development of global groundwater models and big-data assessments of groundwater wells have helped to push the boundaries of our large-scale understanding of groundwater processes. In particular, knowledge of the exchange between surface and subsurface waters is essential for determining the water balance at larger scales. Surface and subsurface water exchanges and inter-catchment groundwater flow affect water, pollutant and nutrient fluxes, bio-organisms in streams, and the groundwater itself. Additionally, human activities (e.g., pumping/irrigation) increasingly affect groundwater flow processes and the exchange between surface and subsurface waters.
In this session, we want to highlight the increasing interest in the large-scale study of groundwater availability, quality, and processes (including groundwater recharge) and discuss current obstacles related to data availability and model design. Therefore, we seek contributions that address issues including:
• Regional to global groundwater-related datasets and big-data assessments
• Transboundary and inter-catchment assessments of groundwater processes
• Identification of dominant controls on groundwater processes across large domains
• Recent methodological developments for inclusion of small-scale hydrological processes into large-scale estimates
• Surface-subsurface water exchange and its effects on hydrological extremes (drought/flood), water availability, and solute and pollutant transport
• Effects of climate change, land use change, and water use change on global groundwater
• Implications of large-scale groundwater understanding on monitoring design, integrated water management, and global water policies
• Large-scale groundwater assessments related to the fulfillment of the UN sustainable development goals (SDGs)
Groundwater recharge is fundamental to supporting sustainable groundwater use for both ecosystems and human water withdrawals. Rates of recharge, and how these rates are affected by climate change, remain poorly constrained due to uncertain models and limited recharge measurements. We develop an emerging relationship between measurements of recharge and climatic aridity. This relationship suggests that recharge tends to be most sensitive to climatic changes in regions where potential evapotranspiration slightly exceeds precipitation. In these regions, even modest aridification can significantly reduce groundwater recharge. Future climate-driven changes in recharge are likely to be primarily influenced by shifts in precipitation, with groundwater recharge typically responding more strongly than the precipitation changes themselves. Measurements of recharge are more sensitive to variations in aridity than recharge simulated by several global hydrological models is. As a result, the impacts of climate change on groundwater replenishment and the sustainability of groundwater use for humans and ecosystems are likely greater than previously estimated.
How to cite: Berghuijs, W., Allen, S., Collenteur, R., Jaramillo, F., Jasechko, S., Luijendijk, E., Moeck, C., and van der Velde, Y.: Climate sensitivity of groundwater recharge, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2546, https://doi.org/10.5194/egusphere-egu25-2546, 2025.
Groundwater is an indispensable part of the global water cycle and an essential water source for domestic, industrial and agricultural use. In England, groundwater is responsible for approximately 30% of public water supply and more than 75% in the most densely populated and water-stressed Southeast. Here, using a new open-access groundwater levels dataset made available by the Environment Agency, we analyse the trends for 2092 stations across England with record lengths ranging from 9 to 189 years. We show that about half of the stations are experiencing a long-term trend or a sudden change and that the long-term trends are very spatially heterogeneous. We also investigate the potential drivers of these trends, and find that the distribution of trends is more likely connected to human activities than to climate or hydrogeological conditions. In particular, we find that stations showing a slow declining trend are concentrated in areas of high irrigation intensity, while stations showing a slow increasing trend are concentrated in densely populated areas. Our results demonstrate that temporal and spatial variability of groundwater trends is dominated by anthropogenic factors.
How to cite: Fang, Q., Rahman, A. S. M. M., Wagener, T., and Pianosi, F.: Human activities dominate groundwater level trends across England, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10233, https://doi.org/10.5194/egusphere-egu25-10233, 2025.
Climate change and anthropogenic activities are increasing stress on groundwater resources, even in generally water-rich areas like Germany, posing significant threats to socio-economic and ecological systems. As the perception of groundwater impacts is often slow and implicit, it results in limited understanding of groundwater drought impacts on socio-economics. To investigate these impacts, we use a unique biweekly dataset of 28,540 shallow and deep groundwater observations from 1950 to 2022 collated from German water authorities. We thoroughly check data quality by conducting outlier tests based on local outlier factor and dynamic time warping, assessing homogeneity of time series by identifying abrupt level shifts and imputing short periods (i.e., 14 days) of missing data by linear interpolation. Based on the reliable dataset, we identify groundwater drought periods along with their severity, duration, and spatial extent. We then link these drought periods to a multi-sectoral drought impact dataset based on newspaper articles from 2000 to 2022 [Sodoge et al., 2023] at a national scale, allowing us to investigate how groundwater drought contributes to socio-economic and ecological impacts (e.g., agriculture, forestry, livestock, wildfires). The results from this study are expected to differentiate the impacts of groundwater drought from other hydrometeorological droughts, provide insights into the long-term impacts of droughts, and help coordinate groundwater resources management and policy development at a national level.
Sodoge, J., C. Kuhlicke, and M. M. de Brito (2023), Automatized spatio-temporal detection of drought impacts from newspaper articles using natural language processing and machine learning, Weather and Climate Extremes, 41, 100574.
How to cite: Wang, Z., Peña Guerrero, M. D., Sodoge, J., Siebert, C., de Brito, M. M., Merz, R., and Tarasova, L.: The Role of Groundwater Drought on Impacting Socio-economics in Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6995, https://doi.org/10.5194/egusphere-egu25-6995, 2025.
This study investigates the groundwater flow trajectories within the Cuitzeo Groundwater Flow System (GFS) in the center of Mexico, the home of the second and third biggest lakes of Mexico. We employ the End-Member Mixing Analysis (EMMA) statistical method, water table configurations and structural features, utilizing semiconservative species such as Sr2+, Li+, and Cl− in order to better understand the pattern of groundwater circulation that is essential for sustainable management of groundwater resources.
Three distinct flow trajectory groups are identified: local, intermediate, and regional, each exhibiting unique hydrochemical characteristics. Local trajectories are linked to recharge waters, whereas intermediate trajectories indicate a progression towards more evolved waters. The regional trajectories, associated with fault zones along the shoreline of Lake Cuitzeo, reveal higher temperatures, suggesting geothermal influences. The lakes were fed by groundwater discharge of different flow paths, Lake Pátzcuaro is fed by local and Cuitzeo by local, intermediate and regional flow paths.
Extensive groundwater extraction, particularly during the dry season and due to the demands of avocado plantations, negatively impacts groundwater and lake levels. This extraction for agricultural purposes significantly alters the natural flow patterns and hydrochemical characteristics of the lakes.
This research highlights the need for integrated water resource management strategies that account for the interconnectedness of local, intermediate, and regional flow systems. Additionally, it brings international attention to the impact of avocado plantations on groundwater systems.
How to cite: Olea Olea, S., Morales-Casique, E., Medina Ortega, P., Ramírez-Serrato, N. L., and Ramírez González, L.: Flow path delimitation in a groundwater flow system discharging into Mexico's majorlakes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2934, https://doi.org/10.5194/egusphere-egu25-2934, 2025.
Global data have served an integral role in characterizing large-scale groundwater systems, identifying their sustainability challenges, and informing on socioeconomic and ecological dimensions of groundwater. These insights have revealed groundwater as a dynamic component of both the water cycle and social-ecological systems, leading to an expansion in groundwater science that increasingly focuses on interactions between groundwater with ecological, socioeconomic, and Earth systems. This shift presents many opportunities that are conditional on broader, more interdisciplinary system conceptualizations, models, and methods that require the integration of a greater diversity of data in contrast to conventional hydrogeological investigations. Here, we identify and review over 140 global open access datasets and dataset collections that span elements of the hydrosphere, biosphere, climate, lithosphere, food systems, governance, management, in addition to other human dimensions and socioeconomic systems relevant to groundwater science. This initiative offers a reference of existing data for use in interdisciplinary groundwater assessments, and summarizes these data across the primary system to which the dataset relates, spatial resolution, temporal range, data type, generation method, level of groundwater representation, and institutional location of lead authorship. At present, our review includes 15 groundwater datasets, 23 datasets explicitly linked with groundwater, and 106 datasets with implicit or potential groundwater connections. The majority of datasets are temporally static, and we find that temporally dynamic data availability peaked over the 2000-2010 decade and has declined since. Furthermore, only a small fraction of temporally dynamic data are explicitly linked to groundwater. We find that most groundwater datasets are generated by a small subset of countries, including the USA, Germany, the Netherlands, and Canada and that many countries facing acute groundwater sustainability challenges are not leading global data collection efforts. We conclude with four potential priorities for future global groundwater data collection, including: elevating regional and local scale perspectives, needs, and data in global initiatives, developing data sharing initiatives providing reciprocal benefits to data providers, more explicit representation of groundwater and uncertainty in global datasets, and the development of groundwater or freshwater system-wide essential variables.
How to cite: Huggins, X. and the initiative's co-authors: A review of open data for studying global groundwater in social-ecological systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20627, https://doi.org/10.5194/egusphere-egu25-20627, 2025.
Groundwater plays a crucial role in meeting both human and ecosystem water needs. Its importance is expected to grow due to increasing water demand and the impacts of climate change on surface water resources, particularly in the Southern Hemisphere, where irrigated agricultural expansion continues to intensify. However, limitations in the spatio-temporal coverage of groundwater monitoring networks constrain our understanding of surface–groundwater interaction dynamics. Here, we present a groundwater well dataset for Brazil. It encompasses compiled and standardized well data from Geological Survey of Brazil projects. The harmonized dataset, which was validated by the Geological Survey of Brazil, underwent rigorous quality assurance and quality control procedures to ensure accuracy, adhering to principles of transparency and data integrity. The dataset includes over 351,000 wells spanning from the early 1900s to 2024, including 472 monitoring wells with daily water level measurements from 2010 to 2024. In addition to information on well location, primary use, and static water level, the dataset includes variables that can support integrated surface and groundwater management, such as distance to the nearest river, land use, and aquifer data. The potential applications of this dataset are wide-ranging. Here, we demonstrate two applications that can be replicated with other groundwater datasets. First, we compared well water levels with nearby river water levels to identify the direction of flow between Brazilian rivers and aquifers. The results indicated that over 55% of the analyzed wells in unconfined aquifers have water levels below those of the nearest river, suggesting that river water may seep into the underlying aquifer. Second, we applied the analytical depletion functions developed by Glover and Balmer to wells in unconfined aquifers to estimate streamflow depletion caused by groundwater pumping. The results suggested that approximately 9% of the analyzed rivers experience a streamflow depletion fraction exceeding 10% of their baseflow. These findings have the potential to enhance the integrated management of surface and groundwater resources in Brazil. Ultimately, we hope this accessible dataset fosters collaboration across the fields of groundwater hydrology, surface water hydrology, and water management.
How to cite: Wendland, E., Uchôa, J. G. S. M., Oliveira, P. T. S., Ballarin, A. S., Meira Neto, A. A., Gastmans, D., Anache, J. A. A., Jasechko, S., and Fan, Y.: A Brazilian groundwater dataset for advancing integrated water surface and groundwater management , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7247, https://doi.org/10.5194/egusphere-egu25-7247, 2025.
Groundwater depth is the result of a balance between climatic conditions (rainfall, temperature, radiation, etc.), topography (slope and proximity of a river), land use, soil and subsoil characteristics (hydrodynamic parameters), and potential exploitation by man.
In order to better understand the evolution of water resources in Africa, the ParFlow-CLM model (Maxwell et al. 2015) was used to provide at high resolution simulation (1 km²) over West Africa (3.5 million km²). This simulation was obtained after a long period of groundwater equilibration using a simplified version of the Parflow model with a monthly time step, and forced by the variable P-ETR (rainfall minus evapotranspiration). Simulation started with a 30 m water table depth everywhere and equilibrium was reached after a few tens of hydrological years in the Sudanian part and 1700 years in the Sahelian part close to the Sahara. The depths of the water table obtained range from 1-2 m to 85 m below the surface. This preliminary operation has a high numerical cost and required just over 1,000,000 hCPUs over 2,560 cores.
In order to reduce computing time and allow modelling of water table depths over the whole of Africa (30.5 million km²), a method based on artificial intelligence (AI) has been applied, following the work of Tran et al. (2021) and Bennett et al. (2024), to reproduce the operation of ParFlow and allow computing times of the order of 1000x less than physical modelling. The approach consists of conducting the automatic learning of the AI on sub-regions of the Parflow simulation, and then evaluating the relevance of the results on other regions.
In this presentation, we show how AI can be used to estimate the equilibrium groundwater depths simulated by the Parflow model over West Africa, while drastically reducing the numerical cost. We also show the performance of this methodology for mapping groundwater depths over the whole of Africa using networks of piezometers and village wells.
Bennett, A., Tran, H., De la Fuente, L., Triplett, A., Ma, Y., Melchior, P., et al. (2024). Spatio‐temporal machine learning for regional to continental scale terrestrial hydrology. Journal of Advances in Modeling Earth Systems, 16, e2023MS004095. https://doi.org/10.1029/2023MS004095
Maxwell R.M., L. E. Condon, and S. J. Kollet (2015). A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3. Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015
Tran, H., Leonarduzzi, E., De la Fuente, L., Hull, R. B., Bansal, V., Chennault, C., et al. (2021). Development of a deep learning emulator for a distributed groundwater–surface water model: ParFlow‐ML. Water, 13(23), 3393. https://doi.org/10.3390/w13233393
How to cite: Pellarin, T., Zoppis, A., Arboleda Obando, P., and Cohard, J.-M.: Mapping the groundwater depth in Africa at high resolution (1 km²) based on the Parflow model and machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16809, https://doi.org/10.5194/egusphere-egu25-16809, 2025.
Groundwater plays a crucial role in drinking water supply, agricultural and industrial production, and ecosystem stability worldwide. Numerical modelling has been applied in past decades to better understand the groundwater flow patterns and future threats to preserving groundwater head levels and quality facing both anthropogenic and natural threats (i.e. aquifer overexploitation and climate change impacts). Most groundwater models focus on local to regional scale groundwater systems, covering areas up to 10,000 km2 and relying on local data to set up and calibrate the groundwater model. However, in recent years several attempts have been made to build a global groundwater model based on global datasets and using a combination of high-performance computing and parallel numerical code. One of the main limitations of this approach is the simplified schematization of hydrogeological heterogeneity in these groundwater models.
Therefore, this work aims to increase the realism and complexity of the hydrogeological schematizations of continental to global-scale groundwater models. To this end, we divide the globe into large-scale groundwater regions and apply a novel approach to estimate the regional-scale hydrogeological makeup of large-scale groundwater models. Three main lithological layers are defined, the most recently deposited unconsolidated sediments represent the top model layer while the second layer consists of older unconsolidated sediments. The third lithological layer consists of sedimentary rock formations, whose depth and type are defined from available global datasets (e.g. GLiM and CRUST 1.0). Additionally, the ArchPy Python library is used to further split these three lithological layers into several sub-layers representing the heterogeneous conditions (e.g. clay or sandy sub-layers). The resulting geological model is then used as a base to build a groundwater and variable density flow model, set up with the parallel iMOD-WQ code. This allows us to simulate complex large-scale groundwater processes with extensive amounts of active model cells and thus provide a better understanding of large-scale groundwater flow patterns. In the next steps, these large-scale groundwater models can be further improved by incorporating local data to create more accurate geological models and to calibrate the groundwater model input parameters.
The presented methodology was applied to create a groundwater model spanning the Australian continent, Papua New Guinea island and the continental shelf connecting these two landmasses. By applying this methodology to other large-scale groundwater regions around the world we can eventually create a new global groundwater model with higher and more realistic hydrogeological complexity and thus provide valuable insight into global groundwater flow patterns and input into Earth system models where groundwater processes are often largely simplified or neglected.
How to cite: Zamrsky, D., Oude Essink, G. H. P., King, J., and Bierkens, M. F. P.: Developing a set of large-scale 3D groundwater models using iMOD-WQ and global datasets – a pathway towards a new global groundwater model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8020, https://doi.org/10.5194/egusphere-egu25-8020, 2025.
The expansion of unconventional oil and gas development (UOGD), made possible by horizontal drilling and hydraulic fracturing of shale formations, has fostered economic benefits in the United States (US), increasing domestic energy supplies and exports for international markets. At the same time, local concerns about risks posed by this industry on the environment and public health persist, especially regarding the potential contamination of drinking water in communities that depend on aquifers for daily use. Quantifying such risks at large scales has been difficult due to spatiotemporal monitoring constraints and challenges in impact attribution. Here, we develop a physically based framework to assess groundwater contamination risks from UOGD in a 300,000-sq km region encompassing three states in the Northern Appalachian Basin, US (Pennsylvania, Ohio, and West Virginia). The region is home to thousands of unconventional wells drilled into the Marcellus and Utica-Point Pleasant Shale, as well as over 4 million residents served by domestic groundwater wells. Our framework integrates publicly available geospatial data with groundwater flow and solute transport modeling. We employed an ensemble calibration approach to derive multiple realizations of model parameters tuned to minimize residuals between simulation outputs and available hydrologic observations. For each realization, forward particle tracking simulations from UOGD well locations were performed to simulate spills and delineate advective transport pathways towards drinking water receptors. Ensemble simulation results were then translated into quantitative metrics of contamination risk. We illustrate how the framework can be applied both ex post, to establish physics-driven pathways between UOGD sources and observed well-water impairments, and ex ante, to identify priority areas for enhanced monitoring and protection. The assessment framework can be used to evaluate risks associated with multiple contaminant sources distributed across large regions.
How to cite: Soriano, M., Wang, C., Van Velden, D., Lin, S., Baker, K., Warren, J., Saiers, J., and Maxwell, R.: Assessment of groundwater contamination risks from shale gas development in the Northern Appalachian Basin, USA, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13979, https://doi.org/10.5194/egusphere-egu25-13979, 2025.
Manganese (Mn) is a highly abundant element in the Earth’s crust that can become enriched in groundwater through reductive dissolution of Mn-containing minerals. Although an essential trace element for humans, manganese poses a health threat mainly in the form of neurotoxicity when consumed in high quantities. Mn has thus far received little attention, particularly in comparison to the other geogenic contaminants of arsenic and fluoride. The World Health Organisation (WHO) recently lowered the guideline from 400 µg/l to 80 µg/l, but it is not known in how many regions of the world the new level is exceeded, let alone how many people are exposed.
We have therefore collected thousands of manganese measurements from Southeast Asia and used machine learning (ML) modelling to investigate the factors related to enrichment in groundwater. By using spatially continuous predictors, we are able to apply the ML model to produce prediction maps of manganese in groundwater. This upscaling of existing manganese measurements provides insights into managanese is nearby areas based on comparable values of the associated predictor variables. The maps are then compared with existing ones of arsenic and iron, which likewise get released in groundwater along the same sequence of biologically mediated redox reactions. By creating prediction maps of both the old and new WHO drinking water guidelines of 400 µg/l and 80 µg/l, respectively, the extent of the increase in areas and populations potentially exposed to hazardous Mn concentrations can be visualized and better appreciated. This serves to raise awareness of the impact that the reduction of the guideline value may pose.
How to cite: Podgorski, J. and Berg, M.: Spatial prediction of geogenic manganese in Southeast Asian groundwater, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17960, https://doi.org/10.5194/egusphere-egu25-17960, 2025.
Groundwater systems are critical for ensuring food and water security while supporting vital ecosystem functions. However, the depletion of aquifers worldwide raises pressing concerns about the sustainability of groundwater withdrawals and environmental flows. Despite ongoing mitigation efforts, a significant gap remains in quantifying their effectiveness. This study focuses on the karstic Edwards Aquifer system in Texas, evaluating the impact of current mitigation strategies on maintaining groundwater levels and spring flows, which are essential for biodiversity and water security. By employing counterfactual artificial intelligence, we address the pivotal question: “What would have occurred, and what might occur, in the absence of these mitigation measures?” This innovative approach provides valuable insights into historical impacts and future scenarios under intermediate- and high-emission climate pathways. By simulating scenarios without mitigation, our analysis highlights the tangible benefits of groundwater management strategies, demonstrating their critical role in enhancing climate resilience and ensuring the sustainability of aquifers.
How to cite: Sharma, C., Başağaoğlu, H., Yoosefdoost, I., Wootten, A., Chakraborty-Reddy, D., Bertetti, F. P., Mirchi, A., and Chakraborty, D.: What If There Were No Mitigation? Counterfactual AI in Groundwater Sustainability Research, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20689, https://doi.org/10.5194/egusphere-egu25-20689, 2025.
Unsustainable groundwater extraction and use is a significant issue in India's arid and semi-arid regions, where surface water is scarce. According to the Central Ground Water Board 2023, groundwater accounts for nearly 62% of irrigation water, serving as a primary source for irrigation and domestic supply. Rajasthan, characterised by its arid and semi-arid climate, faces significant challenges in groundwater management due to increasing water demand, climate variability, and unsustainable extraction practices. The western region is most affected by arid climate and low rainfall, varying from 250 mm to 650 mm annually. Expanding agriculture strains limited freshwater resources in the state, shifting the state's dependency on groundwater and leading to excess groundwater withdrawal (the stage of groundwater development is 148.17% in CGWB report, 2023). This study analyses spatial and temporal patterns of rainfall, temperature, surface water resources and groundwater level fluctuations, as well as water demand (including water demand for agriculture) in the state using finer-scale data. The study also reviews current surface and groundwater resources related policies designed to address water scarcity challenges in the state.
How to cite: Sankhla, N. and Udmale, P.: Spatiotemporal analysis of surface water and groundwater resources in Rajasthan, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16853, https://doi.org/10.5194/egusphere-egu25-16853, 2025.
Groundwater plays an important role in river flow, but groundwater and river flow dynamics have often been considered separately, particularly at large scales. In recent years there has been an increased focus on understanding the influence of groundwater on river flow across different geographies, with the timing and magnitude of this relationship varying between locations and across time. Despite this, finding clear evidence of the specific influence of groundwater on river flow is challenging as no method can fully resolve their relationship. We seek to better understand the role of groundwater in river flow across Europe using data from thousands of catchments. This analysis includes investigation into timescales of catchment memory and the correlation of baseflow conditions to annual and seasonal river flow. We further investigate the spatial nature of these elements, including how these are linked to local catchment properties and the regional associations and larger patterns across catchments.
How to cite: Anand, M. and Berghuijs, W.: How does groundwater affect river flow in Europe?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15196, https://doi.org/10.5194/egusphere-egu25-15196, 2025.
Recording and analysing groundwater level data can reveal the nature and extension of groundwater processes like flow direction and recharge magnitude. The analyses can even indicate the influence of precipitation, evapotranspiration and runoff on the subsurface processes. However, such analyses are only possible when the recorded data cover relatively large periods of time at a suitable frequency.
This work presents a time series analysis of groundwater levels in wells located in typical hydrogeological settings in Sweden. The aim was to identify eventual trends in the natural variations of groundwater levels in relatively deep aquifers. The analysis included identifying possible correlations between groundwater levels, precipitation and evapotranspiration. Additionally, the intention was to evaluate the possible relationships between groundwater levels in deeper aquifers and level variations in surficial glacial deposits. The work addresses also the characteristics of the hydrogeological environments where the observation wells are located. The data was retrieved from twenty wells selected from a database available at the Geological Survey of Sweden (SGU). The results indicate varying correlations between groundwater levels, precipitation and evapotranspiration. This is probably explained by the different hydrogeological environments where the observation wells are located and the respective aquifers’ degree of confinement.
How to cite: Mendoza, A. and Järlefors, V.: Analysis of groundwater levels in various hydrogeological environments in Sweden, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18494, https://doi.org/10.5194/egusphere-egu25-18494, 2025.
Groundwater is a central component of social-ecological systems. However, our understanding of how it is dynamically interlinked with the atmosphere, hydrosphere, cryosphere, biosphere, geosphere, and anthroposphere is limited. Existing datasets lack features that enable us to better understand groundwater functions and how they are affected by anthropogenic change. Specifically, there remains no large-scale groundwater dataset that provides analysis-ready groundwater time series alongside groundwater-associated variables and attributes. In the pursuit of understanding the planet's groundwater dynamics, we present GROW (global GROundWater analysis package). This user-friendly, quality-controlled dataset combines groundwater depth and level time series from around the world with associated social-ecological variables. GROW is designed to enable large-sample spatio-temporal groundwater analysis without much further preprocessing. The dataset contains more than 180,000 time series from 41 countries – whereby over 90 % of the time series are from either North America, Australia or Europe - in a daily, monthly, or yearly temporal resolution. Most of them are between 10 and 20 years long, from 01/1888 to 04/2024, and have a median depth to the water table of 8 metres. Groundwater data is paired with a total of 37 time series or attributes of meteorological, hydrological, geophysical, botanical, and anthropogenic variables (e.g., precipitation, ground elevation, aquifer type, NDVI, land use). More than 20 data flags about well features (e.g., location coordinates and license), as well as time series characteristics (e.g., gap fraction or length), simplify a quick data filtering tailored to specific needs. GROW provides an essential foundation understanding large-scale groundwater processes and provides a robust resource for calibrating and validating models that address groundwater dynamics in social-ecological systems. Gaining an enhanced insight in these processes is essential for managing groundwater resources and ensuring their long-term sustainability.
How to cite: Bäthge, A., Ruz Vargas, C., Lischeid, G., Collenteur, R., Cuthbert, M., Fleckenstein, J., Flörke, M., de Graaf, I., Gnann, S., Hartmann, A., Huggings, X., Moosdorf, N., Wada, Y., Wagener, T., and Reinecke, R.: GROW: A Global Time Series Dataset for Large-Sample Groundwater Studies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17713, https://doi.org/10.5194/egusphere-egu25-17713, 2025.
Abstract
Groundwater withdrawal from confined aquifers is a critical source for irrigation. However, over the past few decades, unsustainable practices have persisted in many large river basins to ensure food security and sustain livelihoods. The current approach to represent the actual groundwater withdrawal in the Community Earth System Model (CESM) is limited, posing challenges to effective groundwater management in irrigation. We integrated global datasets on observed groundwater consumptive use into the CESM coupled with the Community Land Model (CLM5) to address this. These datasets were taken from a published groundwater data inventory for 15,038 national and subnational administrative units globally and reconstructed at a spatial resolution of 0.5° × 0.5°. We evaluated the model’s simulated value against authoritative datasets, including CGWB (for India) and USGS (for the US), to confirm the accuracy of our approach. For instance, in the Central High Plains of the US, the controlled simulation estimated 10 km³ of groundwater use, whereas the experimental setup showed 16.5 km³, and the reported value from the USGS is 18 km³ for 2015. Our global simulation results showed a 28% increase in annual groundwater use for irrigation compared to the controlled irrigation simulation (422.4 km³ vs. 304.5 km³) run for a period of 15 years from 2001 to 2015. Notable cumulative groundwater use differences were observed in 2012 (146 km³), 2011, and 2005 (141 km³ each). Based on the results we identified over 15 major hotspot basins, defined as regions where a majority of grids exhibit a high groundwater abstraction percentage as compared to surface water irrigation. Furthermore, to achieve a sustainable solution we investigated substituting high-water-demand crops with low-water-requirement crops in hotspot regions and simulated groundwater management scenarios for irrigation. Our study provides critical insights into groundwater depletion issues in hotspot basins, highlighting the interconnected dynamics of climate, water resources, and irrigation. These findings contribute to the development of more sustainable water management strategies on a global scale.
Keywords: Irrigation water use, Earth system model, Simulation period, Sustainability
Acknowledgment
This study was supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Science, ICT, and Future Planning (RS-2024-00456724), and Korea Environment Industry & Technology Institute (KEITI) through the R&D Programs for Innovative Flood Protection Technologies against Climate Crisis, and Water Management Program for Drought funded by the Korean Ministry of Environment (MOE) (RS-2023-00218873 and RS-2023-00231944).
How to cite: Panda, M. R. and Kim, Y.: Data-based global groundwater use for irrigation in CLM5: hotspots and sustainability implications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15364, https://doi.org/10.5194/egusphere-egu25-15364, 2025.
In the province of Quebec, Canada, the role of groundwater and its contribution to baseflow are rarely included to assess the vulnerability of surface water sources. However, in the case of Quebec City (560,000 inhabitants), stakeholders prefer that an integrated surface water and groundwater analysis be carried out to meet the highest standards of sustainable management. That approach goes beyond legislation, which does not require a fully integrated study.
A research project has been initiated to develop a set of stakeholder-oriented tools to assess both quantitative and qualitative vulnerability of the city's drinking water sources. The project focuses on the 350 km² catchment of the city’s main drinking water intake, which is in the Saint-Charles River. Due to intensive low flow periods, stakeholders are currently facing quantitative problems, with up to 95% of the river's flow being pumped. It is therefore crucial to characterise the water cycle in the area, including the identification of the main hydrological processes and the estimation of transient water availability. This requires a better understanding of the interactions between surface water and groundwater.
For that purpose, and to assist stakeholders, we developed a 3D integrated surface and subsurface flow model for the catchment with the HydroGeoSphere platform. The model is calibrated to observed times series of water table elevations and stream discharges from a network of monitoring wells and steam gauging stations. We then assess seasonal variations in water balance, resurgence and infiltration rates. Using a hydraulic mixing-cell postprocessing tool, we determine the different fractions of each streamflow component. This highlights the predominance of groundwater at the surface water intake, in agreement with isotopic analyses.
Finally, we also simulate the spatiotemporal vulnerability of the water intake by integrating climate change and urban development scenarios. Our study demonstrates that integrated surface and subsurface hydrological models are valuable tools to assist in designing water source protection plans, paving the way to new resource management policies.
How to cite: Frot, B., Gatel, L., Tremblay, Y., Delottier, H., and Therrien, R.: Source Water Protection in Quebec City: Using an integrated 3D hydrological model to investigate surface water - groundwater interactions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1312, https://doi.org/10.5194/egusphere-egu25-1312, 2025.
With extreme climatic events and population growth predicted to continue increasing over the coming century, water stress across Europe (and elsewhere around the globe) is soon predicted to become critical. Offshore Freshened Groundwater (OFG) is being increasingly identified within continental margin sedimentary sequences worldwide, and has potential to be used as an industrial, agricultural, or potable resource, especially for draught mitigation during extreme climatic events. As part of an international effort under the Horizon Europe Water4All project RESCUE (RESources in Coastal groundwater Under hydroclimatic Extremes), we explore new methodologies to allow for the flexible and rapid identification, assessment, and modelling of OFG systems on a large scale. In this presentation, we share the preliminary work of the RESCUE project in exploring methods for the rapid and semi-automated detection of OFG from well logs, machine learning driven interpretation of seismic reflection data, and integrated porosity/permeability determinations from seismic attributes and well logs. The ultimate goal of this work will be to allow for the rapid generation of large-scale reservoir models, facilitating the dynamic modelling of high resolution and massive OFG systems with Parallel MODFLOW6.
How to cite: Phethean, J. J. J., Li, Z., Bertoni, C., and Lu, Y.: Rapid ML and semi-automated methods for large-scale subsurface data interpretation and reservoir modelling: Applications for offshore freshened groundwater, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4291, https://doi.org/10.5194/egusphere-egu25-4291, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot A
EGU25-15983 | ECS | Posters virtual | VPS11
Groundwater chemical trends analyses in the deep aquifers of the Piedmont Po Plain (NW Italy): preliminary evaluation of ongoing processesFri, 02 May, 14:00–15:45 (CEST) | vPA.10
The Piedmont Plain (NW Italy) is characterized by a shallow phreatic aquifer hosted in fluvial complex (gravel and sand), overlying a fluvial-lacustrine and marine complex (gravel and sand with silty clayey levels) containing deep confined/semiconfined aquifers.
Deep aquifers are essential for the supply of drinking water in the Piedmont Plain. However, detailed information on deep aquifers is lacking, such as a regional piezometric map, a continuous monitoring of the water table variations over time and a regional characterization of GW quality. Moreover, the deep groundwater chemical values in the Piedmont Po Plain show significant temporal variability and need to be characterized.
The aim of this study was to analyze the trends (period 2000–2021) in the main physicochemical parameters (electrolytic conductivity (EC), pH) and main ions (Ca, Mg, HCO3, Na, Cl, NO3 and SO4) in 70 wells in the deep aquifers in order to identify the main ongoing processes. Furthermore, to gain a deeper understanding of specific processes, the temporal distribution of threshold exceedances for the sum of pesticides (period 2009-2021) was evaluated. The potential interaction with shallow aquifers was evaluated making a comparison of the average concentrations for the main ions and parameters between shallow and deep aquifers. In general, shallow aquifers are exploited for agricultural purposes and show higher concentrations compared than deep aquifers.
Additionally, the temporal trends of ion exchange (Ca+Mg/Na index) were evaluated to highlight the contribution from silty-clayey layers, which represent the less permeable portions of the deep aquifers.
Results highlight relevant increasing trends for EC, Ca, Mg and Cl in more than 60% of the monitored wells, and increasing trends for HCO3 and Na in more than 40% of the monitored points. For these parameters, decreasing trends exist for less than 10% of the monitored points. SO4, NO3 and pH show heterogeneous trends. In particular, several monitored wells show significant variation over time, with concentrations doubling from the beginning of the time series. The sum of pesticides shows greater exceedances of the threshold values in the most recent period (2016-2021) compared to the previous one (2009-2015).
The temporal trends of ion exchanges reveal the presence of trends in 61% of the monitored wells, with a prevalence of increasing trends, corresponding to direct ion exchange. For the main ions, the comparison between the average concentrations in the shallow and deep aquifers shows higher values in the shallow aquifers.
These results suggest an increase in the recharge of the deep aquifers by the shallow aquifers and an increased contribution from silty-clayey layers of the deep aquifers. These processes are consistent with excessive withdrawal from deep aquifers. Furthermore, the increasing concentrations represent a significant issue, leading to the progressive deterioration of deep groundwater quality. In conclusion, the main processes responsible for the variation in groundwater chemistry in the deep aquifers were identified, defining the existence of impacting and worrying processes at a regional scale.
How to cite: Cocca, D., Lasagna, M., and De Luca, D. A.: Groundwater chemical trends analyses in the deep aquifers of the Piedmont Po Plain (NW Italy): preliminary evaluation of ongoing processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15983, https://doi.org/10.5194/egusphere-egu25-15983, 2025.
