With the increasing threat of climate change, projections of its impact on the availability of natural resources, such as groundwater, are becoming significantly more important. While extrapolation or prediction is a valid application for environmental system models, their assumptions and limitations are often not clearly communicated or investigated. This is especially true for data-driven models, which have been applied more frequently, and often with great success, to groundwater problems in the last decade. But are these techniques applicable to long-term future predictions of the impact of climate scenarios on groundwater resources, and if so, under which conditions and limitations?
Within the context of KlimaKonform (Technische Universität Dresden, 2023), a research project studying the impact of climate change on the Central German Uplands, ensembles of multi-layer perceptron (MLP) models have been derived to estimate groundwater levels for a variety of wells in a region in Saxony-Anhalt in Germany. Once trained to replicate historical time series with climatological input data such as precipitation and temperature, these model ensembles were then tasked to predict the groundwater levels up until the end of the current century under different climate scenarios.
We analyse the plausibility of the model ensemble predictions to shed light on the above-proposed question: what are factors (and metrics) of success, as well as limitations and possible failures, of data-driven methods (MLPs in this case) for long-term prediction? First, we propose that using ensembles of models, rather than “the single-best” trained model, is a necessity for such applications. We identify different methods of pre-processing of input and target data, structural model setup as well as target-oriented post-processing of ensemble simulations as vital factors for the successful application of MLPs to long-term prediction of groundwater levels under climate change scenarios. We further highlight remaining limitations and pose the question of how they could potentially be overcome.

Technische Universität Dresden, 2023. KlimaKonform: Forschungsprojekt KlimaKonform. https://klimakonform.uw.tu-dresden.de/

How to cite: Gosses, M. and Wöhling, T.: Potentials and limitations of MLPs for predicting groundwater heads under climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5322, https://doi.org/10.5194/egusphere-egu23-5322, 2023.

Groundwater level forecasting with machine learning has been widely studied due to its generally accurate results and little input data requirements. Furthermore, machine learning models for this purpose are set up and trained in a short time when compared to the effort required for process-based, numerical models. Despite the high performance of models obtained at specific locations, applying the same model architecture to multiple sites across a regional area might lead to contrasting accuracies. Likely causalities of this discrepancy in model performance have been barely examined in previous studies. Here, we investigate the link between model performance and the effects of geospatial site characteristics and time series features. Using precipitation and temperature as predictors, we model groundwater levels at approximately 500 observation wells in Lower Saxony, Germany, using a 1-D convolutional neural network with a fixed architecture and hyperparameters tuned for each time series individually. The performances are evaluated against geospatial and time series features using correlation coefficients. Model performance is negatively influenced at sites near waterworks and densely vegetated areas. Besides, the more complex the time series, the higher the metrics, but autocorrelation reduces the model performance. The new insights evidence that further information is required at certain locations to improve model accuracy due to external impacts.  

How to cite: Gomez, M., Noelscher, M., Broda, S., and Hartmann, A.: Performance assessment of groundwater level forecasting with deep learning: a case study of Lower Saxony, Germany., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2696, https://doi.org/10.5194/egusphere-egu23-2696, 2023.

Over last few decades the Punjab region of India has been one of the country's leading contributors to agricultural products. The agricultural farms in the region are supplied with water from a well-established canal system and groundwater reserve in the state. The share of irrigated area in the region fed by canals and groundwater wells are 28 and 72%, respectively. The over and unscientific usage of groundwater over the years has resulted in groundwater depletion at an alarming rate. To help policymakers address the situation and develop effective plans, forecasting groundwater recharge for the future is utmost essential. The recharge process primarily governs the growth or depletion of groundwater reserve. Groundwater recharge is one of the most difficult phenomena to be quantified as it cannot be measured directly and is influenced by several processes varying spatially and temporally. Extensive research work for quantifying the groundwater recharge have been performed in the past. These investigations introduced a number of methodologies, including chemical tracers and physical procedures. These methods, however, being experimental in nature, involve significant time and investment. The use of machine learning algorithms to predict the recharge is promoted as a solution to these problems. These algorithms have proven to be efficient enough to deduce the recharge with very high accuracy. Through a variety of models, ranging from the most basic to one of the more intricate, we have attempted to forecast the recharge scenario in the Punjab region, India. Four machine learning algorithms, namely the Multi-linear regression model, Non-linear regression model (Random Forest), Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) have been employed in this study. The aim was to comprehend the dependence of groundwater recharge on the factors of temperature, precipitation, soil type, LULC, and ground slope. The observed recharge for every subsequent month in a 30-year period is calculated using the observed monthly groundwater level data from observation wells located throughout Punjab. The monthly temperature and precipitation data are used for the study while soil type and ground slope for the location of the observation stations are extracted from digital elevation models (DEMs). At intervals of three years, the LULC maps are created. The models are then used to forecast and compare with the available observation data after the entire data set was split into a training and testing set using the 80/20 method. The models were then assessed for their ability to predict observational data using the Root Mean Squared Error (RMSE) and Coefficient of Determination (R²) in each case. The groundwater recharge prediction is then performed using the model with the highest accuracy.

How to cite: Banerjee, D. and Ganguly, S.: Predicting future groundwater recharge scenario in the Punjab region of India using machine learning techniques, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-361, https://doi.org/10.5194/egusphere-egu23-361, 2023.

We determine the sensitivity of groundwater-induced flooding forecast to different modelling strategies (evolving model structure and quantity/nature of data). We test three calibration strategies on shallow coastal aquifers:

• we use the river network as a proxy of aquifer seepage and determine uniform hydraulic conductivity and porosity. Despite limiting assumptions of surface/groundwater connections, this strategy could be broadly deployed with rapid advances in temporal and spatial river mapping at regional to national scales.
• we calibrate uniform conductivity and porosity on hourly piezometric data time-series close to the seashore, which are controlled by both tide and inland aquifer recharge fluctuations. We investigate the limitations of the uniform assumptions and demonstrate the interests of coastal and inland forcing as complementary sources of information.
• we introduce spatially variable conductivities and porosities and use additional inland piezometers. Hydraulic parameters are mapped to the main geological structures of the studied area.

We analyse the results of these 3 competing strategies on the hydraulic parameters and, in a more prospective way, on the spatial distribution of vulnerabilities to groundwater fluctuations. We perform our analysis within the “Rivages Normands 2100” research project, in several sites of Western Normandy (France). In this area, low-lying coastal areas and neighbouring lands are prone to groundwater table increase, leading to flooding of buried networks and building foundations. The site of Saint-Germain-sur-Ay (66 km² coastal watershed) is equipped with 6 piezometers from which we collected 1.5 year-long hourly time series of groundwater level. It includes a low-elevation coastal area made up of sands and a continental area made up of schists.  The modelling approaches allowed us to simulate groundwater levels up to 2100, and to analyse the associated evolution of vulnerability. Simulations are performed using Modflow on a daily timestep, with a 75 m spatial resolution.

Results obtained from the 3 models consistently show that vulnerable areas are mostly clustered close to the shoreline. We also show that, in the studied watershed, the first calibration strategy using the river network data leads to long-term simulations close to the second calibration strategy using the piezometric data of the coastal area. Integration of extra piezometric data from the continental area in the third strategy provides more reliable simulations. This analysis is progressively deployed to other sites and extended to cost-benefit analyses including the costs of site instrumentation and the benefits of forecast reliability. Integration of flood zones aerial photography during extreme events is being implemented to the first modelling strategy, as well as wetland mapping.

How to cite: Le Mesnil, M., de Dreuzy, J.-R., Aquilina, L., Gresselin, F., and Gauvain, A.: Calibration of coastal hydrogeological models for the analysis of groundwater-induced flooding: from instrumentation to model reliability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12633, https://doi.org/10.5194/egusphere-egu23-12633, 2023.

Modeling spatially continuous variables from point measurements is integral to environmental scientific research in many fields. This is especially the case for groundwater, which is accessible only at boreholes and springs. Often, further management decisions are dependent on spatially continuous values of groundwater level or quality parameters.

For this task, deterministic or geostatistical interpolation methods are traditionally used, involving the spatial structure of the point locations as a set of XY-coordinates. In this case, the spatial model is usually created based solely on the geographic location of the measurement and the spatial autocorrelation of the target values. With few exceptions (e.g. co-kriging), classical interpolation techniques do not support the incorporation of covariates that are spatially correlated to improve spatial prediction accuracy.

Spatial predictions using machine learning (ML) models are an attractive and increasingly prevalent alternative, which use correlated, spatially continuous covariates (e.g. meteorological data, land-use, or geological maps) as predictors for groundwater level or quality parameters. They are trained on the nonlinear relationship between these predictors and the target values with the available point measurement data. However, spatial autocorrelations of the target values are usually not considered, as the points are treated independently of their location. Therefore, the machine learning models cannot exploit and represent the spatial dependence structures in the target data, without any further information about the geographic locations. The incorporation of locational information into ML models is, however, not trivial. From other fields, diverse approaches (e.g. using coordinates directly, distances to certain locations in space, Euclidean distance matrices, transformed coordinates) exist, and also different assessments of their suitability. For example, it is often stated that XY-coordinates are very well suited for the use with decision trees, but not for neural networks.

We systematically investigate the impact of the most commonly applied methods for the integration of spatial information, such as XY-coordinates, transformed coordinate-based input features, Euclidean distance matrix or distances to corners or center, Wendland transformed coordinates, and combinations of the aforementioned, on the interpolation results for selected hydrogeological parameters and different test sites in two ML models (Random Forest and Multi-Layer-Perceptron). We compare the results by cross-validation and with kriging reference models as well as visual assessment for plausibility.

The results show that the incorporation of spatial covariates can significantly improve model performance, especially when the data have high spatial autocorrelation (and the data set is sufficient to capture this). In particular, the Euclidean distance matrix and Euclidean distances to defined locations proved to be efficient approaches to provide the spatial data structure for the model, while the application of XY-coordinates often resulted in significant artifacts in the resulting prediction surfaces.

How to cite: Ohmer, M., Doll, F., and Liesch, T.: Incorporating spatial information for regionalization of hydrogeological parameters in machine learning models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12842, https://doi.org/10.5194/egusphere-egu23-12842, 2023.

Estimating the potential of underground water sources has become a top priority for authorities in semi-arid mountain regions, particularly in the context of recent climate change that has made surface water less accessible and available. However, exploratory methods are typically quite expensive, such as geophysical and topographic methods, which take into account the vastness of the terrain and the extremely limited accessibility in the mountains such as the High Atlas. To achieve this, it has become necessary to use indirect methods first to define potential groundwater zone boundaries before turning to direct measures. This study proposes an indirect, straightforward, quick, and minimally costly method for identifying potential areas for groundwater in the Rherhaya watershed, High Atlas-Morocco, using machine learning, geographical information systems, and remote sensing.

Machine learning algorithms have been increasingly applied to define potential groundwater areas mapping. It’s performance to predict the spatial distribution of potential groundwater zones was tested with an inventory-based spring’s geodatabase. 254 spring’s obtained inventory was split into two independent datasets, including 70% of the springs for the training set and the remaining 30% of springs for validation purposes in the test set. 19 layers of landslide-conditioning factors were prepared and checked for collinearity issues, to produce the potential groundwater zones map. The conditioning factors were selected, prepared and classified, in order to determine the contribution of each class of factors to potential groundwater areas, including: Elevation, Aspect, Slope angle, Curvature plan, Curvature profile, Stream Power Index (SPI), Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), Distance to rivers, Lithology, Rainfall, Land Use and Land Cover (LULC), Drainage density, Valley Depth, Topographic Position Index (TPI), Terrain Ruggedness Index (TRI), Slope Length (LS), Geomorphons, Distance to faults, Distance to spring and Distance to roads. Both the inventory and the conditioning factors are obtained from the observation and interpretation of different data sources, namely high-resolution satellite images, aerial photographs, topographic maps, and extensive field surveys.

Using RStudio software, three machine-learning algorithms were used in this study: Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). The model’s evaluation and validation was assessed using a hybrid approach based on k-cross validation, ROC Curve - AUC, and confusion matrix for the estimation of the predictive performance. The three models provided very encouraging results in terms of identifying the areas that are very likely to produce subsurface water in the Rherhaya basin.  The main contributing factors to the potentiality of underground waters, according to the three methods, are the valley depth, TPI, distance to rivers, and curvature plane.  With an AUC of 84.4% in the test data, it was clear that the SVM model outperforms the other models for the 70/30 percent subdivision. These findings may contribute to the development of a significant database that will help in the management of water resources in vulnerable areas by decision-makers.

How to cite: Jadoud, M., El Achheb, A., Laftouhi, N., Namous, M., Khouz, A., Trindade, J., El Bchari, F., Bougadir, B., Eloudi, H., and Rachidi, S.: Machine Learning Models for Spatial Prediction of Groundwater Potentiality in a Large Semi-Arid Mountainous Region: Application to the Rherhaya Watershed, High Atlas, Morocco, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16375, https://doi.org/10.5194/egusphere-egu23-16375, 2023.

Since there are many uncertainties in the state of groundwater, after determining the research direction and objectives, a numerical model should be established by means of simulation to analyze the hydraulic characteristics such as transmissivity and storativity in the research area. Sensitivity analysis is carried out effectively, and the optimal pumping test strategy is designed based on it. In many previous studies, the sensitivity map made by the two-dimensional model was used to discuss the sensitivity. However, after a long period of data accumulation, it was found that although there would be a continuous change in the discharge, the pumping well and the vicinity of the observation well's hydraulic gradient did not change significantly. From this we get an inference, that is, we cannot obtain the exact location of the sensitivity source in space by simply using a two-dimensional sensitivity map, at most, only the average parameter value of the active area can be obtained. In addition, most of the current research is based on the assumption of homogeneous steady-state conditions, but groundwater mostly exists in the form of heterogeneous transient state in nature. Therefore, this research hopes to use the method of inverse calculating the heterogeneous field to plan and use the developed software (VSAFT2 and VSAFT3) for effective 2D and 3D space simulation, so as to use the characteristics of the three-dimensional space concept to confirm various information and extend the discussion of the source. This will have a great contribution to the changes of water bodies in the future analysis area, groundwater recharge patterns and diffusion control of underground pollution.

How to cite: Kuo, J.-T., Wen, J.-C., and Lin, H.-R.: Sensitivity analysis and research of hydraulic characteristic parameter and observation wells during the pumping test, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2998, https://doi.org/10.5194/egusphere-egu23-2998, 2023.

Groundwater is one of the most valuable resources in the US. The United States Geological Survey (USGS) reported that about 26% of the water used in 2015 came from groundwater. Due to the scarcity of groundwater observations, it is still challenging to monitor groundwater resources at the watershed scale (where local decision making occurs). In addition, for long-term high-resolution simulations, physically-based models become very computational demanding and data hungry. With much less computational time and physical knowledge, machine learning (ML) techniques are able to capture complex nonlinear connections between groundwater dynamics and atmospheric and land surface processes from historical data. Recent studies have shown their success in groundwater modeling.

In this study, we develop a ML-based estimator to produce water table depth (WTD) estimates over the Contiguous US (CONUS) at a spatial resolution of 1 km using USGS WTD observations and other hydrometeorological data. The WTD estimator consists of two components. One component captures spatial variations in WTD using random forests and the other component learns temporal variations in WTD by Long Short-Term Memory networks. We combine the results from the two components to obtain WTD estimates. The estimated WTD are compared to USGS WTD observations to show their reliability. Based on the WTD estimates, we study groundwater changes in the Upper Colorado River Basin (UCRB) in recent drought years, which is one of the principal headwater basins in the US. To better interpret the performance of the WTD estimator, we conduct sensitivity analyses on input variables for both components. Moreover, we assess the uncertainty of the WTD estimator in estimating WTD over the CONUS. Our study demonstrates that the WTD estimator can generate reasonable WTD estimates over CONUS, thereby facilitating understanding of groundwater systems in the US. The WTD estimator can also be transferred to other regions in the world that have a similar hydrologic regime to a region in the US.

How to cite: Ma, Y., Leonarduzzi, E., Defnet, A., Melchior, P., Condon, L., and Maxwell, R.: The Development of a ML-based Estimator to Reconstruct Water Table Depth over the Contiguous US, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10596, https://doi.org/10.5194/egusphere-egu23-10596, 2023.

We applied different machine learning algorithms to predict the groundwater water fluctuation in a semi-arid river basin. Precipitation, temperature, evaporation, relative humidity, soil type, and groundwater lag were modeled as input features. Feature importance analysis of the input features indicate that the groundwater lag is the most relevant whereas the soil type is the least relevant input features. We applied the backward elimination approach to eliminate the less relevant features in mapping groundwater. Using the relevant input features we trained different machine-learning models (random forest, decision tree, neural network, linear regression, ridge regression, support vector regression, k-nearest neighbors, recurrent neural network). All these algorithms predicted the groundwater level with a high correlation of coefficient (R) ranging from 0.83 to 0.91 and a Root Mean Square Error (RMSE) ranging from 1.61 to 2.17 m.  We found that the random forest algorithm outperforms the other algorithms with a RMSE of 1.61 and R = 0.91. 

How to cite: Bhadani, V., Singh, A., Kumar, V., and Gaurav, K.: Machine learning models to predict groundwater level in a Semi-arid river catchment, Central India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12629, https://doi.org/10.5194/egusphere-egu23-12629, 2023.

Abstract: Groundwater depletion is a serious threat to agriculture and economic development, with an adverse impact on the ecological environment in Beijing. With the successful implementation of the South-to-North Water Diversion Project (SNWDP), groundwater (GW) depletion is expected to be alleviated. Here, we bridged the gap of the monthly terrestrial storage anomaly (TWSA) observations of the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On (GRACE-FO) missions, then downscaled the spatial resolution of GW storage anomaly (GWSA) from 0.5°×0.5° to 0.25°×0.25° in Beijing using deep learning (DL) method, and precisely quantified the characteristics and causes of GWSA before and after the SNWDP, including that 1)  reconstructed 0.5°×0.5° TWSA in Beijing during 2004 to 2021 using three DL architectures (Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), Multilayer Perceptron (MLP)); 2) selected the optimal performance of DL for downscaling GWSA from 0.5°×0.5° to 0.25°×0.25°; 3) quantitatively analyzed of GWSA characteristics before and after the SNWDP basing on Random Forest (RF). The results indicated that the trend of downscaled GWSA was consistent with in-situ grounder water level measurements, while the seasonal amplitude differed. Before the SNWDP (2004-2014), the GW in Beijing showed a declining trend with a rate of -20.8 mm/yr, of which human factors contributed 75.7% (21.9% and 21.1% for domestic water and agricultural water, respectively), and climatic factors accounted for 24.1%. After the SNWDP (2015-2021), GW gradually increased by 39.3 mm/yr, and GW was significantly restored, of which the human factors accounted for 59.2% (30.7% and 14.2% for domestic water and water diverted to Beijing by the SNWDP, respectively). Our research could provide a new reliable theoretical support and technical reference for GRACE/GRACE-FO dynamic monitoring of groundwater.

Keywords: Beijing GW storage; spatial resolution downscaling; DL; GRACE/GRACE-FO; SNWDP

How to cite: Hu, Y., Chao, N., Wang, J., Liu, Z., and Zou, K.: South-to-North Water Diversion weaken groundwater depletion in Beijing: insight from downscaling GRACE/GRACE Follow-on data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6165, https://doi.org/10.5194/egusphere-egu23-6165, 2023.

Chronic consumption of elevated concentrations of fluoride in groundwater can cause detrimental health effects including dental mottling and skeletal fluorosis. However, the concentration of fluoride is not known in many aquifers. To help address this, we have used machine learning to create a global fluoride prediction map based on the WHO drinking-water guideline of 1.5 mg/L. Over 400,000 data points of fluoride in groundwater (10% greater than1.5 mg/L) from 77 countries were used along with 12 predictor variables out of an initial set of 62 spatially continuous variables relating to geology, soil, climate and topography. The model performs very well, (e.g. AUC of 0.90) and was used to produce a global prediction map. This helps gauge the scope of the problem and identify potential hotspots that should receive the focus of more groundwater testing, including parts of central Australia, western North America, eastern Brazil and many areas of Africa and Asia. This fluoride hazard model was also used to estimate the global at-risk human population at about 180 million people, most of whom live in Asia and Africa. Another model was created using additional physicochemical parameters measured in situ. Although this model (AUC of 0.95) could not be used to create a map, it helps to better understand the processes related to the dissolution and accumulation of fluoride. For example, both the spatially continuous and in-situ predictor variables confirm that arid conditions promote the dissolution of fluoride in groundwater.

How to cite: Podgorski, J. and Berg, M.: Global machine-learning model of naturally occurring fluoride in groundwater, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12956, https://doi.org/10.5194/egusphere-egu23-12956, 2023.

As one of the vulnerable arid regions, Saudi Arabia suffered from an optimal water crisis. Rapid population, industrialization, and urbanization mandated the water stress and agricultural water balance in the region; as such, there is a need for integrated water resources planning and management. To meet sustainable development goal six (SDGs-6), the ground and surface water need to be within the required quality and quantity. Hence there is a need for both the physical, chemical and hydrogeological assessment of groundwater in the Eastern part of Saudi Arabia. This study proposed three scenarios to assess the hydrogeological groundwater quality, namely, experimental laboratory based on fieldwork, geospatial mapping of the parameters (EC, pH, CaCO₃, Turbidity, NA, k, Mg, Ca, F, Cl, Br, Li, B, Al, V, Cr, Fe, Mn, Ni, Co, Cu, Zn, As, Se, Sr, Mo, and Ba), and intelligent computational analysis using machine learning (ML). This research is motivated toward intelligent prediction of some heavy metals using neural network (NN), and adaptive neuro-fuzzy inference system (ANFIS). The evaluation criteria of the predictive results were analyzed based on mean absolute percentage error (MAPE), correlation coefficient (CC), and determination coefficient (DC). The outcomes proved the satisfactory ability of the NFIS model over the NN approach, despite its predictive credit. 

How to cite: Yassin, M., Abba, S., Usman, A., and Aljundi, I.: Spatiotemporal and hydrogeological assessment of groundwater supported by soft computing modeling of heavy metal in Al-Hassa, Eastern Province, Saudi Arabia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4745, https://doi.org/10.5194/egusphere-egu23-4745, 2023.

Processes controlling surface-groundwater interaction in terms of recharge and baseflow have been a topic of pursuit in the hydrological research community. The groundwater recharge and baseflow in hard-rock aquifers is significantly impacted by rainfall pattern, aquifer characteristics, weathering/soil condition, topography, land use and land cover. Analysis of the recharge and baseflow process in tropical semi-arid hard-rock aquifer regions of southern India is crucial due to heavily tailed monsoon system prevailing in the region, heterogeneity of aquifers in terms of fractures and lineaments and presence of several man-made irrigation tanks along with the drainage network. Process uncertainties in groundwater recharge simulation due to impact of climate change on vegetation and resulting changes in evapotranspiration can significantly impact groundwater projections. Poor representation of diffused and focused recharge pathways and inadequacy in capturing the feedback among climate, land use and groundwater systems are other factors introducing uncertainties. Finally, the gaps in long-term observational data make it challenging to assess the impact of climate change. A taluk scale study conducted in Karnataka State of India indicated a significant correlation between the rainfall intensity distribution and climatology on groundwater recharge in Hard-rock aquifers. The current study targets to add a baseflow component represented by a 2D groundwater model and extend the previous study to a larger scale. The primary objectives of the study are to estimate the long-term groundwater recharge and baseflow trends and evaluate their association with rainfall and climatological variability. As the projected climate scenarios reflect higher frequency of high-intensity rainfall, it becomes essential to evaluate the impacts of varying rainfall patterns on the surface-subsurface processes.

How to cite: Goswami, S. and Sekhar, M.: Understanding impact of Rainfall Intensity Distribution and Climatology on Recharge and Baseflow in tropical Hard-rock Aquifers of South India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-619, https://doi.org/10.5194/egusphere-egu23-619, 2023.

The groundwater level is a comprehensive response of the external stimuli of the aquifer, such as rainfall and river recharge, and is one of the main factors affecting the formation deformation. The spatiotemporal analysis of stratum elastic deformation identifies different stimulus sources and their influence on groundwater level changes, as well as the influence of groundwater level changes on stratum elastic deformation. In addition to the numerical test to verify the correctness of the analysis process, this study uses the daily water level observation data of each aquifer in Yunlin area of Taiwan to conduct principal component analysis to explore the influence of rainfall and river water level on the variation of groundwater level in each aquifer. Furthermore, based on the magnetic ring layered subsidence observation well data, the iterative principal component analysis was used to explore the influence of the variation of the groundwater level in each layer on the elastic deformation of each layer.

The research results show that the first principal component of the groundwater level in each aquifer in the Yunlin area can fully reflect the impact of rainfall on the groundwater level, and the residual water level affected by rainfall can be deducted to analyze the impact of the river water level. The results show that the shallower water level Compared with the deeper aquifers 2-2 and 3, layers 1 and 2-1 have a more significant effect on the river water level. In the analysis of formation compression deformation, the correlation analysis between the principal component of groundwater level and the principal component of formation deformation shows that there is a high negative correlation between the water level change of each aquifer and the formation deformation, which is in line with the physical mechanism of groundwater level and formation deformation. The above analysis results show that principal component analysis, combined with the analysis process of this study, can indeed identify and isolate the influence of various external stimuli to the groundwater level, and can also obtain the main changes in the elastic deformation of the formation, which can be used as the basis for further water resources planning and analysis. Important reference.

How to cite: Chang, L. C., Chen, Y. C., and Lin, T. C.: An application of iterative principal component analysis on analyzing the regional groundwater level and stratum elastic-deformation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11745, https://doi.org/10.5194/egusphere-egu23-11745, 2023.

Following the evolution of the hydrodynamic parameters (hydraulic conductivity and storativity) of an aquifer over time is difficult and does not exist for an aquitard. The primary reason is the small number of observations, which are mainly pumping or slug tests. However, solutions exist to recover these parameters using only groundwater level monitoring data at a sampling rate of 1 hour, data which is extensively available. These solutions take advantage of the boreholes’ response to different tidal phenomena, including oceanic, earth and atmospheric tides.

Martinique Island, in the Lesser Antilles, is a very interesting field to study these techniques, since 16 years of piezometric level data have been recorded on this volcanic island in a monitoring network of 29 boreholes. The variety of aquifer geometry and geology enables to study different types of responses, in addition the island is submitted to seismic activity that may impact aquifers and particularly their hydraulic conductivity.

The method consists in computing amplitude and phase response of aquifers to both atmospheric, earth and ocean tides. Then, the response of semi-confined aquifers to different loading sources at the tidal frequencies (between 1 and 2 cycles per day) is modelled. The model is a general and adaptable one, in order to consider the various geometries (including the presence or not of an aquitard) and aquifer boundary conditions (confined, semi-confined, connected to the atmosphere or not). Finally, a careful inversion is done to obtain the characteristics of the aquifer.

Our results show that the Martinique aquifers present responses to different sources of loading. We show that using the dominant source response alone, we are able to recover aquifer or aquitard hydraulic conductivity and its evolution over the last 16 years, which we compare with pumping tests results. Using a combination of sources, we are also able to recover elastic properties like the evolution of the loading efficiency and shear modulus. Finally, comparing different sites responses, we are able to assess which aquifer is more vulnerable to pollution through vertical leakage within their aquitard.

How to cite: Thomas, A., Fortin, J., Vittecoq, B., and Violette, S.: Passive characterization of aquifers hydro-mechanical properties using tidal signals, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8098, https://doi.org/10.5194/egusphere-egu23-8098, 2023.

Interferometric Synthetic Aperture Radar (InSAR) technology has been used to detect the location and magnitude of ground deformation for the past 30 years, providing cost-effective measurements with a fine resolution and precision within centimeters under ideal conditions¹. Persistent Scatterer InSAR Interferometry (PS-InSAR) is an InSAR algorithm that has been developed to overcome decorrelation due to changes in the physical characteristics of the surface over time that limit the InSAR applications ^2,3.

PS-InSAR processing has been used to identify multiple localized land subsidence in the Antwerp and Leuven areas in Belgium. In Antwerp, the harbour was gradually developed, leading to dock excavations in a compressible estuary polders environment and PS-InSAR was used to detect, map and study the ground displacements⁴. In Leuven, significant subsidence was observed through the city and the suburbs, potentially due to delayed consolidation in compressible, low permeability aquitards. One of the possible cause of these subsidence phenomena is related to variation in groundwater levels resulting in consolidation processes. To test this hypothesis, geomechanical calculations coupled to groundwater flow models are carried out to simulate the vertical displacements. The results are compared to PS-InSAR-derived subsidence observations for a better understanding of subsurface consolidation mechanisms.

However, there are several practical and conceptual challenges that must be considered when comparing InSAR measurements to results from hydrogeological and geomechanical models. One issue is the choice of the appropriate modeling scale, as subsidence may occur locally but also regionally as influenced by groundwater pore pressure variations occurring at different scales. Another challenge lies in the selection of the appropriate conceptual assumptions linked to the groundwater flow and geomechanical models. Indeed, in addition, uncertainty in the model parameter values is a typical source of uncertainty in the model results. There may also be errors in the InSAR measurements due to various factors such as atmospheric effects and changes in the surface roughness. All these challenges must be taken into account when comparing InSAR measurements to model results.

In conclusion, comparing InSAR measurements with hydrogeological and geomechanical modeling results can provide valuable insights into the actual mechanisms of subsidence. However, it is important to carefully consider the practical and conceptual challenges and limitations linked to this interesting comparison.

References:

¹ Peng, M., Lu, Z., Zhao, C., Motagh, M., Bai, L., Conway, B. D., & Chen, H. (2022). Mapping land subsidence and aquifer system properties of the Willcox Basin, Arizona, from InSAR observations and independent component analysis. Remote Sensing of Environment, 271, 112894.

² Ferretti, A., Prati, C., & Rocca, F. (2000). Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Transactions on geoscience and remote sensing, 38(5), 2202-2212.

³ Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on geoscience and remote sensing, 39(1), 8-20.

⁴ Declercq, P. Y., Gérard, P., Pirard, E., Walstra, J., & Devleeschouwer, X. (2021). Long-term subsidence monitoring of the Alluvial plain of the Scheldt river in Antwerp (Belgium) using radar interferometry. Remote Sensing, 13(6), 1160.

How to cite: Moreau, A., Choopani, A., Declercq, P.-Y., Orban, P., Devleeschouwer, X., and Dassargues, A.: Difficulties arising when PS-InSAR displacement measurements are compared to results from geomechanical and groundwater flow computations., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16384, https://doi.org/10.5194/egusphere-egu23-16384, 2023.