Aquifers are crucial resources for mitigating the impact of droughts, which are expected to exacerbate in the future. Nevertheless, in many cases, there is not enough monitoring data to define distributed models to forecast future potential groundwater (GW) levels. In this work, we apply and compare different approaches for short-term predictions of GW levels. We use conceptual models (e.g., AQUIMOD, EMM, etc) and machine learning techniques (e.g., NAR, NARX, ELMAN, LSTM and/or GRU). We follow the next steps: calibration/training, validation and testing of the behaviour of conceptual models and neural networks for short-term prediction. We consider exogenous variables (precipitation, temperature, recharge, etc.) for short-term prediction. Multiple series of exogenous variables have been generated by using a stochastic weather generator, which will be used to perform a stochastic forecast. The results will be analysed for dry, medium and wet seasonal horizons. The predictions made with "machine learning" are compared with those generated by the conceptual models. In order to assess potential impacts of Climate Change on GW levels, we simulated some simulated some future local climate scenarios within the conceptual models. We analyzed the robustness of the results and their uncertainty. The risk of droughts has also been studied by evaluating the severity of droughts from the series generated by applying the “SPI” indices to the generated series. The method has been applied in two aquifers, namely Campo de Montiel (Center Spain) and Vega de Granada (Southern Spain).

Acknowledgments: This research has been partially supported by the projects: STAGES-IPCC (TED2021-130744B-C21) and SIGLO-PRO (PID2021-128021OB-I00), from the Spanish Ministry of Science, Innovation and Universities.

How to cite: Baena-Ruiz, L., Collados, A. J., Pulido-Velazquez, D., Gomez-Gomez, J. D. D., Baca Ruiz, L. G., and Pegalajar Jimenez, M. C.: Short-term and Long-term Groundwater level forecast by applying conceptual and neural networks approaches., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20895, https://doi.org/10.5194/egusphere-egu24-20895, 2024.

Mismanagement of groundwater resources leads to groundwater depletion and other environmental issues such as quality reduction and subsidence. Water table prediction is essential for optimal management of groundwater resources. Hence, artificial intelligence (AI) methods have been used widely to predict water tables in recent years. This paper adopted some new methods of machine learning, e.g., categorical boosting (CATBoost), extreme gradient boosting (XGBoost), and Convolutional neural network-Long Short-Term Memory (CNN-LSTM), to predict water table. The key input parameters were evaporation/transpiration, rainfall, temperature, and water table in the prior month. To better compare the models, simulations were executed in daily and monthly periods. DeLuca Preserve located in Florida was selected to test the proposed algorithms.  The results indicated that in general machine learning algorithms are appropriate approaches to predict water tables. CNN-LSTM algorithm with RMSE = 0.22 m and R² = 0.96 showed better performance in predicting daily groundwater levels.  However, monthly water tables were predicted much better using CATBoost algorithm with RMSE = 0.11 m and R² = 0.99.

How to cite: Javadi, S., Najafi, M., Golmohammadi, G., Mohammadi, K., and Neshat, A.: Forecasting Groundwater Level in Florida using Advanced Machine Learning Approaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11326, https://doi.org/10.5194/egusphere-egu24-11326, 2024.

Effective groundwater management requires accurate prediction of GroundWater Level (GWL) fluctuations. This study proposes six novel regression algorithms by integrating a Fuzzy Inference System (FIS) with six Nature-inspired Algorithms (NiA) to enhance GWL prediction accuracy. The proposed algorithms contribute to several Nature-based Solutions (NbS) goals, including improving water security by ensuring that groundwater resources are used sustainably and helping to ensure that people have access to clean and safe water. In this study, we coupled FIS with Invasive Weed Optimization (IWO), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), Differential Evolution (DE), Harmony Search (HS), and Weevil Damage Optimization Algorithm (WDOA). We used precipitation, relative humidity, and groundwater level lag as potential input features to predict the groundwater level. We found that the Fuzzy-IWO-GWL model accurately predicts GWL fluctuations, achieving a high correlation coefficient (R = 0.89), low normalized root mean square error (nRMSE = 0.18), and minimal bias (bias = 0.08). A comparative analysis involving eleven benchmark algorithms (consisting of standalone and deep learning algorithms) reveals the superior performance of the proposed algorithm. This study highlights the potential of Nature-Based Solutions and nature-inspired algorithms in groundwater management applications, providing valuable insights for policymakers and stakeholders involved in ensuring groundwater sustainability.

How to cite: Singh, A., Bhadani, V., Kumar, V., and Gaurav, K.: Groundwater level prediction using hybrid ML: Bridging the gap between nature-based solutions and nature-inspired algorithms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-495, https://doi.org/10.5194/egusphere-egu24-495, 2024.

Groundwater recharge estimates can be obtained from time series of hydrometeorological variables and groundwater levels using a data-driven approach that combines a root-zone model with a lumped-parameter groundwater model. This approach was successfully tested at an agricultural site where lysimeter data of seepage is available (Collenteur et al. 2021). At that site, groundwater levels are assumed to be solely driven by recharge from precipitation. Frequently, however, groundwater levels are affected by other hydrological stresses, for example, resulting from stream-aquifer interaction or direct human impacts such as water abstraction or construction activities. To assess the method under more complex conditions, we evaluate the recharge estimates obtained from the model application to a multitude of groundwater monitoring wells located in the urban area of Graz (Austria) and the semi-urban and agricultural area south of the city (Kokimova et al. 2022). Possible influences from the River Mur, including changes in hydraulic structures, were taken into account in the model, whereas other stresses were insufficiently known and thus ignored. The resulting recharge estimates show a wide range, with values in agricultural areas often plausible. However, at some locations, particularly in the urban area, extraordinarily high values that appear implausible were estimated. Besides the possible impact of unknown water abstraction and construction activities, the simplified representation of the urban recharge processes may explain these findings. Even where the model failed to provide reasonable recharge estimates, the results support the identification of possible local influences on groundwater levels, which should be further investigated to enable a better assessment of groundwater recharge, particularly in the urban area.

Collenteur, R., Bakker, M., Klammler, G., Birk, S. (2021): Estimation of groundwater recharge from groundwater levels using non-linear transfer function noise models and comparison to lysimeter data. Hydrol. Earth Syst. Sci. 25: 2931-2949. doi: 10.5194/hess-25-2931-2021

Kokimova, A., Collenteur, R., Birk, S. (2022): Data-driven time series modeling to support groundwater model development for the Grazer Feld Aquifer. EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7734, https://doi.org/10.5194/egusphere-egu22-7734

How to cite: Birk, S., Kokimova, A., and Collenteur, R.: Applicability of groundwater recharge estimation from time series modeling of groundwater levels in a (semi-)urban area, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10638, https://doi.org/10.5194/egusphere-egu24-10638, 2024.

Wetlands, which are systems with significant environmental value, can be very sensitive to global change. The inundated area of wetlands, reflecting water quantity, stands as a key variable in the decision-making process for evaluating sustainable management strategies in these ecosystems.
Satellite optical sensors are effective for regional and global surface water monitoring. However, depending on the satellite, they may not offer a comprehensive long-term time series of inundated areas to study the effects of global change. This limitation arises from factors such as presence of clouds, sensor failure, low revisit time or spatial resolution, or recent launch. 
We propose leveraging hydro-climatological data to enhance and complement satellite-observed inundated area dynamics. In this approach, we evaluate the effectiveness of classical methods such as ARIMA and multiple regression models, along with advanced techniques like artificial autoregressive neural networks and other machine learning algorithms. The goal is to integrate covariate information and simulate extensive and continuous inundated area time series. This methodology is valuable not only for filling gaps in observational data but also for projecting the impacts of climate change on inundated area in wetlands.
The suggested methodology was implemented in the Lagunas de Ruidera wetland area in south-eastern Spain. This region exhibits a significant natural interplay between groundwater and surface water, highlighting a conflict between groundwater-dependent ecosystems and groundwater extraction for irrigation. From January to June, the average observed inundated area is approximately 4.3 km². In summer, there is a reduction of about 13% in the surface water area, which is subsequently recovered during the autumn.

Acknowledgments: This research has been partially supported by the project SIGLO-PRO (PID2021-128021OB-I00) funded by the Spanish Ministry of Science, Innovation and Universities and the project C17.i7.CSIC – CLI 2021-00-000 funded byEuropean Union NextGenerationEU/PRTR.

How to cite: Collados-Lara, A.-J., Aguilera, H., Pulido-Velazquez, D., Pardo-Igúzquiza, E., Baena-Ruiz, L., Gómez-Gómez, J. D. D., Mejías, M., and Grima, J.: Modelling inundated area in wetlands combining satellite and hydrological data: A comparison of classical methods and machine learning algorithms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14793, https://doi.org/10.5194/egusphere-egu24-14793, 2024.

Groundwater flow in folded and faulted terrains is governed by the geological structure, which exerts a significant influence on the flow directions and on the spring location.  Numerical modeling can be challenging because the complex geometry of model layers can result in steep inclination of aquifer bed and different thicknesses of saturated portions within the same numerical layer. Drying and rewetting of cells during model iterations leads to numerical instabilities, increasing numerical error and runtime. However, excessive simplification of the system, seeking for numerical stability, may lead to an unsatisfactory predictive capability of the model.

Recent advances have addressed such issues, including the development of solvers that facilitate convergence and/or reduce computational errors due to model nonlinearities [1], [2].

The aim of this research was to develop and test a procedure for the simulation of groundwater flow in a complex karst, folded, multilayer aquifer, while minimizing numerical errors and runtime.

We applied this procedure to a 3D model, previously developed in steady state conditions with an equivalent porous media approach using the Newton-Raphson formulation of MODFLOW-2005 (MODFLOW-NWT) [3]. The major impact of folded and faulted geological structures on controlling the flow dynamics in terms of flow direction, water heads, and spatial distribution of the outflows to the river and springs was accounted for in a numerical model where three aquifer layers and two semipermeable layers have been constructed respecting their true geometry as far as possible. 

A transient simulation was performed on this model using monthly stress periods and variable pumping simulated by MODFLOW’s WEL package to test effects of withdrawals for water supply on the aquifer system. Initial runs showed a very high mass balance error (2% discrepancy over cumulative volume) and a runtime of 1 hour and 38 minutes. To reduce both mass balance error and runtime, the USGS software for the optimization of the MODFLOW-NWT solver inputs, NWTOPT [4], was used.  NWTOPT identified improved solver inputs, which gave a superior tradeoff between acceptable mass balance error (-0.39%) and much reduced runtime (40 minutes and 9 seconds).

The added stability and shorter runtimes are particularly welcome if many iterations of the model were needed for automated calibration, application or uncertainty analysis.

References

[1]  Niswonger, R. G., Panday, S., & Ibaraki, M. (2011). MODFLOW-NWT, a Newton formulation for MODFLOW-2005. US Geological Survey Techniques and Methods, 6(A37), 44.

[2] Hunt, R. J., & Feinstein, D. T. (2012). MODFLOW-NWT–Robust handling of dry cells using a Newton Formulation of MODFLOW-2005. Ground Water, 50(5), 659-663.

[3] Preziosi, E., Guyennon, N., Petrangeli, A.B., Romano, E., Di Salvo, C. (2022) A stepwise modelling approach to identifying structural features that control groundwater flow in a folded carbonate aquifer system. Water, 14 (16), art. no. 2475,  DOI: 10.3390/w14162475

[4] Newcomer, M. W., & Hunt, R. J. (2022). NWTOPT–A hyperparameter optimization approach for selection of environmental model solver settings. Environmental Modelling & Software, 147, 105250.

How to cite: Di Salvo, C., Hunt, R. J., Newcomer, M., Feinstein, D. T., and Preziosi, E.: Numerical models of groundwater flow in folded and faulted aquifers in mountainous areas: trade-offs between numerical error and runtime, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17093, https://doi.org/10.5194/egusphere-egu24-17093, 2024.

Nitrate (NO₃⁻-N) stands as one of the prevalent chemical contaminants in groundwater, posing potential repercussions on both the environment and public health. However, the monitoring of this parameter on a national scale is notably limited, especially in developing regions. To address this gap, we applied distinct machine learning (ML) algorithms (Extreme Gradient Boosting, Boosted Regression Trees, Random Forest, and Support Vector Machines) capable of quantifying/predicting NO₃⁻-N concentrations in groundwater. These algorithms were validated through comprehensive application across Mexico. The models initially considered 68 covariates and identified significant predictors of NO₃⁻-N concentration spanning from climate, geomorphology, soil, hydrogeology, and human factors. We achieved an outstanding performance with about 10 times less availability of information compared to previous large-scale assessments, and thus efficiently countered the challenge of limited data availability/monitoring stations. Our success can be attributed mainly to the implementation of the 'Support Points-based Split Approach' during pre-processing, which effectively transformed the limited national groundwater quality database into spatial points suitable for appropriate train/test datasets. Areas exhibiting NO₃⁻-N concentrations exceeding the drinking water standard (>10 mg/L) were identified, notably in the north-central and northeast regions of the country, linked to agricultural and industrial activities. Individuals living in these regions face potential exposure to elevated NO₃⁻-N levels in groundwater. These NO₃⁻-N hotspots align with reported health implications such as gastric and colorectal cancer. This study not only showcases the potential of ML in data-scarce regions but also provides actionable insights for policy and management strategies.

How to cite: Mahlknecht, J., Torres-Martínez, J. A., Mora, A., Kumar, M., Kaown, D., and Loge, F. J.: Data-driven models for groundwater nitrate contamination prediction: A nation-wide approach for Mexico, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12713, https://doi.org/10.5194/egusphere-egu24-12713, 2024.

The accelerated global development process has further intensified climate change, leading to a continuous rise in global temperatures and an exacerbation of climate change severity. This has resulted in an increased frequency of extreme hydrological events. Taiwan, influenced by complex terrain and uneven rainfall distribution, faces challenges in effectively storing rainfall, with individual rainfall allocations falling below the global average. In situations of insufficient surface water supply, groundwater becomes a crucial water source due to its low cost, resistance to pollution, and convenient accessibility. However, prolonged excessive extraction of groundwater not only causes land subsidence but also poses risks of severe disasters such as seawater intrusion.

Taiwan’s groundwater is widely distributed and abundant, especially in some regions with advanced agriculture, where it becomes an indispensable key water resource. However, in the context of climate change, rainfall characteristics are subtly and gradually changing, which in turn has an indirect impact on groundwater resources. This study area is the Choshui River Basin located in central Taiwan. We utilize transfer entropy to analyze time series data from groundwater and surface water resources. Transfer entropy is a statistical masure used to quantify the directional flow of information between systems. This method excels in analyzing complex systems where traditional linear methods might fall short, offering insights into how one system influences or is influenced by another over time. It can be adeptly employed to analyze the long-term interaction mechanisms between groundwater and surface water. It specifically investigates the interrelationships and variations in regional groundwater levels during wet and dry seasons, both temporally and spatially. Through an integrated analysis of methods and relevant results, the study aims to explore the primary factors influencing groundwater variations, comprehend trends in groundwater level changes, and provide crucial information on groundwater characteristics. This study contributes to the optimization of the joint allocation and utilization of surface water and groundwater, and can serve as a reference strategy for the allocation and management of regional groundwater.

How to cite: Chen, G.-Y. and Chang, L.-C.: Investigating Long-Term Interrelation between Groundwater and Surface Water Using Transfer Entropy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17563, https://doi.org/10.5194/egusphere-egu24-17563, 2024.

Groundwater is a vital water source in semi-arid and arid regions characterized by little and erratic precipitation and ephemeral river flow. In these regions, water scarcity is becoming more critical due to population growth, increasing irrigation demands, and climate change. Groundwater recharge here primarily occurs as episodic events and specifically as focused recharge during high river flow, but the controlling processes are poorly understood. Thus, understanding and quantifying the relationships between climate, streamflow and groundwater dynamics is crucial to assess the impact of climate change on future water availability. Historically, water resources assessments in Africa have relied on large-scale hydrological models, however, they often lack validation from groundwater observations. Here, we take an observation-based approach to identify climatic controls on streamflow and groundwater dynamics in the semi-arid Hout-Sand River catchment, Limpopo, South Africa. Using data spanning from 1940-2023, we analyze time series of precipitation, air temperature, stream discharge, and groundwater level and evaluate long term trends and relationships across a range of climatic indices and hydrologic and groundwater signatures. While we find no significant trends in long-term annual precipitation, the precipitation patterns are becoming increasingly extreme and exhibit higher intensities with longer dry periods. In response, streamflow patterns are changing towards longer no-flow periods although there is no significant trend in total annual flows. Long-term groundwater levels are not unanimously increasing or decreasing. However, we observe a dependence of streamflow and groundwater levels on multi-annual patterns of climate variability. Furthermore, preliminary results suggest that episodic precipitation and streamflow events contribute to the majority of total groundwater recharge, and that the recharge mainly occurs close to streams, i.e. as focused recharge. Finally, we aim to use machine-learning regression techniques to identify the most important controls on focused and diffuse recharge in order to perform spatial regionalization.

How to cite: Bjerre, E., Enemark, T., Jessen, S., and Høgh Jensen, K.: Climatic controls on streamflow and groundwater dynamics in a semi-arid catchment: Long-term trends and importance of episodic events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8584, https://doi.org/10.5194/egusphere-egu24-8584, 2024.

This study used groundwater memory timeseries as a proxy for groundwater drought vulnerability to unveil dominant patterns and their correlations with physiographic and climatic controls within hydrogeological systems of Baltic states. Clustering of groundwater memory timeseries was performed to identify regions with similar hydrodynamic systems, while random forest classification identified significant site-descriptive features, giving insights into system similarities and dissimilarities. Catchment characteristics showed the greatest importance followed by climate, topography and land use features. Moreover, spatial analysis of cluster distribution revealed feature combinations which are not covered by groundwater observations, presenting a challenge in understanding groundwater dynamics in these data-scarce locations. The study underscores the importance of considering not only locations with groundwater level data but also regions of typical feature patterns without monitoring infrastructure, thus identifying possible locations for new groundwater monitoring wells. 

How to cite: Bikše, J., Retike, I., and Haaf, E.: Exploring Groundwater Memory in Baltic States: Controls and Complexities of timeseries analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11916, https://doi.org/10.5194/egusphere-egu24-11916, 2024.

This study represents a thorough investigation into groundwater potential within a substantial basin situated in the western region of Saudi Arabia. Fueled by the escalating demand for potable water, agricultural irrigation, and industrial applications, a profound understanding of the capacity of underground water reservoirs, or aquifers, is imperative. Leveraging advanced techniques, this research integrates Geographic Information System (GIS), the Analytic Hierarchy Process (AHP), and remote sensing data to conduct a comprehensive assessment and mapping of groundwater potential zones (GWPZ). The evaluation process involves the meticulous analysis of several thematic layers: geology, slope, land use, lineament densities, soil characteristics, drainage density, and rainfall. The AHP method is then employed to assign weights to each class within the thematic maps, considering the unique characteristics of each parameter and its consequential influence on water potential. The resultant GWPZ map classifies the region into five distinct zones: very low, low, moderate, high, and very high, providing a nuanced understanding of groundwater potential across the basin. By leveraging the synergy of GIS, AHP, and remote sensing data, this research contributes valuable insights into the nuanced intricacies of groundwater potential assessment. The developed methodology can be adapted for similar regions globally, emphasizing the importance of integrating cutting-edge technologies to address critical challenges associated with sustainable water resource management amidst increasing global demands. The findings of this research can inform decision-making processes for sustainable water resource management in the basin, helping to prioritize areas for groundwater development and conservation efforts.

How to cite: Al-Areeq, A., Benaafi, M., Al-Suwaiyan, M., Chowdhury, S., and Aljundi, I.: Remote Sensing-Enhanced Assessment of Groundwater Potential Zones in the Wadi Waj, Western Saudi Arabia: An AHP-GIS Framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4730, https://doi.org/10.5194/egusphere-egu24-4730, 2024.

Groundwater resources in the Nairobi Aquifer Suite (NAS), Kenya, face significant problems largely due to rapid urbanization and the rising water demand. The depletion of groundwater resources at the local level could potentially extend to regional extents, and hence affect natural water flows. This therefore calls for the prediction of aquifer hydrogeological parameters for sustainable groundwater management. This study aims to utilize GIS-based spatial interpolation methods for the in-depth analysis of NAS hydrogeological parameters. Classical geostatistical tools are employed to develop models that can be used to accurately predict hydrogeological parameters of the NAS. Field-measurable predictors, that is, geographic position, elevation, depths and first water struck level, are used to demonstrate the efficacy of the predictive models. Data from hydrogeological measurements, geological surveys and satellite imagery are integrated during the development of the predictive models for key hydrogeological parameters, including, groundwater level, discharge, drawdown, electrical conductivity, and transmissivity. Classical geostatistical tools such as kriging and natural neighbour interpolation are applied to develop spatially explicit maps of the NAS hydrogeological parameters. The distribution of borehole data is analyzed using geostatistical tools such as trend analysis and semi variogram. Cross-validation has been performed to identify the most suitable spatial interpolation model. While, in general, the prediction worked well based on model evaluation metrics such as mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and coefficient of determination (R²), during the testing we observed characteristic deviations from the measured values at some locations. These differences could be due to the geological setting; however, a few outliers may appear due to yet unknown reasons. Further studies utilizing machine learning techniques are expected to develop accurate predictive models that can help in sustainable groundwater management in the NAS. The generated spatial maps provided insightful information on the spatial distribution of hydrogeological parameters in the NAS, facilitating the accurate identification of prospective locations for ideal groundwater extraction.

Keywords: GIS; hydrogeological parameters; Nairobi Aquifer Suite; machine learning; predictive modelling; spatial mapping

How to cite: Kahuthu, D. W., Amimo, M. O., Oiro, S., and Székely, B.: Analysis of Hydrogeological Parameters of the Nairobi Aquifer Suite Using GIS-Based Spatial Interpolation Methods , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-899, https://doi.org/10.5194/egusphere-egu24-899, 2024.

Excessive groundwater extraction, often leads to changes in aquifer-system layers, causing  anthropogenic-triggered land subsidence. The reduction in pore pressure due to groundwater withdrawal acts as an external factor, while soil compressibility serves as the primary internal factor of land subsidence. The interaction between stress and strain within an aquifer-system, or specific layers, is typically represented through stress-strain curves. These curves, illustrating the hydrograph data (stress induced by piezometric level variations) against land subsidence compaction records (strain), offer valuable insights into the geomechanical behaviour of the aquifer-system, such as elastic, plastic, elasto-plastic, or visco-elasto-plastic behaviour. Additionally, these curves can be employed to estimate hydrogeological parameters like the storage coefficients of the aquifer-system. Traditionally, determining storage coefficients from stress-strain curves has relied on subjective visual assessments by skilled researchers. In this study, we proposed a MATLAB© application designed to automate and streamline the analysis of land subsidence datasets. The application facilitates the exploration of potential correlations with piezometric levels and allows for the estimation of storage coefficients. This approach reduces the time-cost of analysis and minimizes potential human-interpretation errors. The developed application integrates temporal series of groundwater levels from observation wells and ground deformation measurements o automatically generate stress-strain curves. To illustrate and validate the effectiveness of the proposed application, the proposed app is applied to diverse aquifer-systems worldwide, each exhibiting distinct geomechanical behaviour. The results showcase the tool's capability in efficiently studying and understanding land subsidence, providing a valuable resource for scientists and researchers investigating the impacts of excessive groundwater extraction on land deformation. 

How to cite: Navarro-Hernández, M., Pozo, S., Valdes-Abellan, J., and Tomás, R.: Automated analysis of strain-stress curves for aquifer system characterization , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4174, https://doi.org/10.5194/egusphere-egu24-4174, 2024.

Sustainable groundwater management requires accurate and reliable prediction of long-term aquifer water balances. This can be achieved with catchment-scale groundwater balance models such as the recently developed Lumped Geohydrological Model (LGhM) (Ejaz et al., 2022, Journal of Hydrology). As a lumped model similar to hydrological rainfall-runoff models, the LGhM offers high computational speed, lower data requirements compared to spatially explicit groundwater flow models, and suitability for uncertainty analysis. Unlike rainfall-runoff models, LGhMs allow simulating total groundwater storage (TGS) by incorporating additional terms for water budget and dedicated, distributed groundwater storage boxes, inspired by the catchment's aquifer characteristics.

Calibration of LGhMs requires both river discharge data and TGS data. LGhMs have shown remarkable performance in synthetic studies (MODFLOW generated data for calibration and validation). The remaining challenge is therefore to obtain TGS data for calibration without full groundwater flow models. Geostatistical methods can help here. They can directly estimate groundwater surfaces from well-based time series, and TGS can then be obtained through spatial aggregation. In this study, we employ space-time kriging to estimate TGS and quantify associated uncertainties. To enhance TGS predictions, we integrate hydrogeological information into the kriging model. These include spatial and temporal trends and soft information inspired by hydrological ideas, such as digital elevation maps, river exchange components, aquifer confinement, and boundary conditions.

The Wairau Plain aquifer in New Zealand serves as the testing ground for this approach, where an existing MODFLOW model provides data for calibration and validation for proof of concept. Once validated, this method can be applied in regions without pre-existing groundwater flow models.

How to cite: Ejaz, F., Wildt, N., Nowak, W., and Wöhling, T.: Estimating catchment-wide total groundwater storage via space-time kriging provides calibration data for catchment-scale groundwater balance models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10521, https://doi.org/10.5194/egusphere-egu24-10521, 2024.

 This research primarily focuses on predicting and analyzing land subsidence and groundwater level caused by the groundwater abstraction for snow melting during winters in Nagaoka City in Niigata Prefecture , Japan. In this area, significant inconvenience caused by heavy snowfall during winters has led to the adoption of groundwater extraction for snow melting purposes. This countermeasure results in seasonal fluctuations in groundwater levels, contributing to plastic compaction occurrences during winter seasons every year. The uncertainty in modeling land subsidence due to the inability to accurately determine the distribution of subsurface physical property values poses a challenge. Additionally, inadequate data regarding the amount of water extracted for snow melting further complicates the analysis.Combining groundwater level data obtained through Kriging interpolation, we utilized numerical simulations by MODFLOW with subsidence package (IBS) and adjusted model parameters to reproduce the scenarios in this region. The model was calibrated to reproduce groundwater level and subsidence data. By referencing parameters in the existing literatures parameters and experimenting with various scenarios, the model successfully simulated changes in groundwater levels and land subsidence, demonstrating the effectiveness of the model. Moreover, by assigning different permeability coefficients in possible ranges, we determined the maximum extent of snowmelt water's contribution to groundwater replenishment. Through the analysis of various extraction and replenishment schemes, we clarified the main causes of land subsidence and the primary sources contributing to groundwater recovery. We also proposed possible extraction strategies to address the challenge of groundwater extraction under acceptable land subsidence considering the increasing demand for groundwater due to urbanization and possible climate change.

How to cite: Yang, Y. and Aichi, M.: Seasonal variation of land subsidence caused by the groundwater abstraction for snow removal in Nagaoka city , Japan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5629, https://doi.org/10.5194/egusphere-egu24-5629, 2024.

Surface hydrological models usually define their modelling units using topographic catchment  boundaries, which are then connected by the river network. Inter-catchment Groundwater Flows (IGFs) are water fluxes that do not respect these topographic boundaries. They can significantly influence river discharge. Therefore, surface hydrological models usually estimate IGFs indirectly, by adjusting the water balance across the topographic catchment. As they cannot be measured directly, the realism of these simulated fluxes can be questioned.

Here, we investigate how a model calibration strategy could help to improve the physical realism of simulated IGFs. We propose a multi-objective calibration strategy, where we optimise the model simulation on two fluxes: river discharge and actual evapotranspiration using MODIS satellite estimates. Indeed, we hypothesize that better IGFs could be estimated if the water balance is more constrained by evaporation. We explore different objective functions to identify the most efficient way to use satellite data by looking at the model robustness in time and space.

The Seine catchment is characterised by a complex, multi-layered aquifer system where the river loses water in some places and gains water in others. We evaluate the ability of the GRSD model, a semi-distributed hydrological model that implements the lumped GR5J in each subcatchment, to consistently describe this system thanks to this calibration strategy. The influence of four upstream dams is also considered in the modelling, as they have a significant impact on the hydrology. In particular, this work could help to understand the extent to which low flows are maintained naturally by groundwater or artificially by these dams.

This work is partly funded by the ANR (CIPRHES project) and by the European Union’s HORIZON Research and Innovation Actions Programme under Grant Agreement No. 101059372 (STARS4Water project).

How to cite: Hsu, S.-C., de Lavenne, A., Andréassian, V., Rabah, A., and Ramos, M.-H.: Better mapping of groundwater-surface water exchanges over the Seine River catchment in a surface hydrological model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15833, https://doi.org/10.5194/egusphere-egu24-15833, 2024.

In general, some parameters assumption or model simplifications are needed for hydrogeological simulation by the limitations of computing loading and lack of survey data. In addition, the hydrogeological simulation result and other geological investigation information are usually displayed separately, which are not able to provide a comprehensive interpretation. However, geological modelling technique provides a solution for these limitations.

In this study, a conglomerate and sandstone distributed site in Taoyuan city, Taiwan, was applied for hydrogeological model establishment, flow simulation, and particle tracking calculation by FracMan software. Furthermore, regional investigation data including geological map, resistivity imaging profile (RIP), geological drilling, and hydrogeological simulation result, were applied to SKUA-GOCAD and integrated into a three-dimensional geological model, which provided comprehensive interpretations. At last, the lithology distribution model built by SKUA-GOCAD was applied to FracMan for another case simulation. This result was compared with the previous result simulated with simplified geological model, showing that how much geological modelling can help hydro-geological simulation. 

How to cite: Chen, L.-G. and Tong, C.-Z.: Combination of Hydrogeological Simulation and Geological Modeling: A Case Study of Conglomerate and Sandstone Distributed Site., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5019, https://doi.org/10.5194/egusphere-egu24-5019, 2024.

Ascending near-surface groundwater is an important source of water supply to crops and grasslands. Tapping this sub-surface water source, they exhibit a more stable productivity over time as compared to groundwater-distant sites. Assessing crop and grass productivity at high resolution with the help of simulation models therefore requires groundwater table distance information in an apposite spatial and temporal resolution. Groundwater level monitoring networks offer point-based data with variable observation frequency and data quality, impairing assessments of local variations in the hydrographs at each observation well. We propose an efficient and structured process for applying principal component analysis (PCA) in optimizing the groundwater level monitoring network. The PCA functions were used to determine the relative contributions of individual observation wells in determining the spatio-temporal variations in hydrographs. For each well, the Principal Components (PCs) derived from the PCA were used as predicted variables to draw reference hydrographs that describe the expected normal behavior of individual observation wells. This reference hydrograph was then compared with the observed hydrograph so that the residuals describe the local deviations from the normal behavior of the observation well. Deriving a time series of the residuals facilitates a rapid screening for idiosyncrasies unique to each well. Based on a ranking of all wells in the network according to their degree of deviation from the reference, we discarded irrelevant monitoring wells and time series. In a case study using 1300 observation wells in Brandenburg State, Germany, with mean monthly data from 2000 to 2022, we showed in preliminary results that the overall difference in groundwater level between the original observation well network and the optimized network developed with PCs is less than 5%, while the total number of observation wells in the network is reduced by 10%, which will save the time and cost to monitor groundwater levels in the area.

How to cite: Raza, A., Baatz, R., Inforsato, L., and Nendel, C.: Optimization of Data from Local Groundwater Head Monitoring Network Using Principal Component Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7598, https://doi.org/10.5194/egusphere-egu24-7598, 2024.

Machine learning approaches have gained high notoriety to approximate computationally-expensive models in the geosciences. Surrogate models are trained using input-output pairs to emulate the numerics of full complexity models. These fast models then assist in forward and inverse uncertainty quantification for various applied problems. However, large input dimensions, typically found in groundwater modelling for very heterogeneous environments, present a challenge for surrogate models. Input dimension reduction (IDR) methods, such as the Karhunen-Loéve expansion (KLE), are known to reduce the number of input parameters used to train surrogate models, while also generating stochastic realizations of the input random fields for groundwater modelling applications. Traditionally, KLE truncates the input parameters such that 90% of the input variance is considered. However, in some applied cases, this dimension remains too large for reliable surrogate model training. Specifically, using a smaller number of input parameters (considering a smaller percentage of the input variance) may introduce IDR-associated errors in the surrogate output. These errors are often overlooked when assessing uncertainty in surrogate model outputs and could be particularly significant in Bayesian inverse modelling. We are offering a surrogate modelling framework tailored for high-dimensional problems that accounts for IDR-induced errors in the context of Bayesian inverse modelling. Our framework allows for more informed decision-making when using surrogate models as approximators and to widen the scope in which surrogates can be used in heterogeneous media applications. We demonstrate the introduced approach using a groundwater flow and transport model with a heterogeneous hydraulic conductivity field to estimate contaminant concentrations and pressure head values.

How to cite: Morales Oreamuno, M. F., Oladyshkin, S., and Nowak, W.: Error-aware surrogate modelling with input dimension reduction for groundwater modelling in heterogenous media, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12586, https://doi.org/10.5194/egusphere-egu24-12586, 2024.