The world largely relies on groundwater extraction for drinking water supply, which is also the case in Germany. In the EU, the Water Framework directive regulates the standards for a chemically good state of water bodies. Thresholds are often exceeded due to fertilizers and pesticides. Methods to assess groundwater vulnerability to contamination to chemical compounds are mainly index-based, GIS-overlay tools. Other approaches are process-based leaching models and statistical approaches. Commonly used index methods remain conceptual in nature and lack validation with monitoring data. Process-oriented approaches tend to focus on the soil layer. Statistical approaches remain underexplored. In the project FARM (Groundwater vulnerability assessment during authorisation procedure of pesticides), we aim to improve groundwater protection by developing a data-based vulnerability index that exploits the existing extensive data bases and covers all relevant environmental conditions and agricultural inputs.

The federal groundwater quality monitoring infrastructure and of water suppliers delivered data of about 26.000 sites which are sampled for about 500 different pesticides. For pesticide monitoring data in Germany, such an exhaustive national database is unprecedented. Given this vast data set, this project aims to apply a fully data-driven approach to identify previously unknown, relevant factors and their interactions that drive groundwater vulnerability to pesticides. We aim to investigate the complex interactions between subsurface (soil, hydrogeology) and surface (meteorology, land use, crop sequences, agricultural practices) site characteristics with the physical-chemical properties (mobility, persistence) of pesticides. The potential of this data set will be exploited by the development, testing and validation of a supervised a machine-learning (ML) model. After an initial feature selection procedure, a Bayesian convolutional neural net will be trained on groundwater quality data and the mentioned extensive catalogue of features. This set-up takes the uncertainty into account introduced by the large percentage of left-censored data (concentrations below limit of quantification of the analytical method). High interpretability of the ML-model is essential, identified factors need to be comprehensible and actionable for decision-makers. We are dealing with a highly heterogeneous, asymmetric monitoring data set and strong biases in many variables are expected. This project thus pioneers in assessing the potential and suitability, as well as limitations and pitfalls of training neural nets on the status-quo of groundwater quality monitoring in Germany. A second major outcome of the project are specific recommendations on adjustments of the national monitoring (spectra of sampled substances, sampling frequency and timings, addition or reduction of monitoring wells in specific areas).

How to cite: Cooke, A.-K., Willkommen, S., and Broda, S.: First steps towards a data-driven groundwater vulnerability index for pesticides in Germany using probabilistic neural networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5569, https://doi.org/10.5194/egusphere-egu23-5569, 2023.

A new clustering strategy was developed and tested using Self-Organizing Maps (SOM), an unsupervised Artificial Neural Network (ANN) type, for identifying zones with similar contamination characteristics within an aquifer. The Gabros de Beja aquifer system (GBAS), located in the Alentejo region, Portugal, was selected as a case study due to its vulnerability to diffuse pollution from intensive agriculture. The proposed methodology consists of: (a) selection of the most representative groundwater contaminants in the aquifer area (i.e., nitrates, sulfates and chlorides); (b) determination of the Natural Background Level (NBL) of the selected groundwater compounds; (c) computation of the ratio between the median concentrations of the groundwater compounds being analyzed and their respective NBL concentration; and finally, (d) application of the SOM clustering technique to group homogenous contaminated areas within the aquifer. The NBL illustrates what thresholds are likely signs of anthropogenic effect by indicating how high or low a parameter's value would be expected under natural geogenic conditions and therefore was used as a first normalization of the dataset. For this methodology, the NBL was computed as the 90th percentile concentration of the selected compounds in piezometers within the study area that presented a median nitrate concentration smaller than 10 mg/L. Nitrate, sulfate and chloride concentration medians from 45 piezometers were used. The results show that the SOM network classified the piezometers into six classes (CL1 to CL6). The least contaminated clusters were CL1 (8) and CL4 (17), with all three compounds presenting median concentrations around 50 mg/L, which for nitrate is the threshold for drinking water limits. CL5 (5) reached median nitrate concentrations above 100 mg/L, while chlorides and sulfates remained below 50 mg/L. CL2 (6) showed an increase in chloride concentration to 100 mg/L, with the other two compounds' concentrations below 65 mg/L. CL3 (3) presented the highest salinization levels reaching chloride concentrations above 180 mg/L, with sulfates around 80 mg/L and nitrates around 50 mg/L. Finally, CL6 (6) presented median levels of the three compounds above 80 mg/L. The most contaminated groups (CL3, CL5 and CL6) were present in sedimentary and weathered metamorphic lithologies, which present high hydraulic conductivities, coinciding either with urban or agricultural areas associated with large-scale irrigation schemes, reinforcing the anthropogenic source of the contaminants. Hence, this study presented a clustering framework that, by reducing the dimensionality of the original dataset, helps to establish a priority list of polluted areas with different degrees of contamination, which is indeed essential for implementing monitoring and management measures for attenuating groundwater pollution.  

How to cite: Medeiros do Nascimento, T. V., Condesso de Melo, M. T., and Proença de Oliveira, R.: Framework for clustering groundwater quality using Self-Organizing Maps to improve aquifer monitoring and management: a case study of the Gabros de Beja aquifer system, Portugal, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-564, https://doi.org/10.5194/egusphere-egu23-564, 2023.

Geostatistical methods are increasingly used in earth sciences and engineering to improve space and time predictions. During mining activities, it is essential to monitor contaminant concentrations in soil and groundwater and estimate their spatial distribution in the area to guide environmental monitoring and reclamation once mining operations have been finished. In this work, we present the geostatistical analysis of the groundwater content in certain pollutants (Cd and Mn) in a group of adjacent mines. The available monitoring locations were Sixty-two. The challenge in this work is the grouped location of monitoring stations within the borders of the adjacent mines. This work aims to map the spatial distribution of Cd and Mn concentrations in groundwater in the entire mining area. The Correlation between Cd and Mn was investigated during the preliminary analysis of the data and found significant. The logarithm of the data values was used, and after removing a linear trend, the variogram parameters by means of a spherical model were estimated. In order to create the necessary contaminants concentration maps, we employed the Ordinary Kriging (OK) method and inversed the transformations. Cross-validation shows promising results (ρ = 92% for Cd and ρ = 88% for Mn, RMSE = 5.1 ppm for Cd and RMSE = 18.2 ppm for Mn), while the uncertainty was calculated in acceptable bounds.

How to cite: Varouchakis, E., Diamantopoulou, E., and Pavlides, A.: Geostatistical analysis of groundwater data in a mining area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11472, https://doi.org/10.5194/egusphere-egu23-11472, 2023.

The East Kazakhstan region is one of the most industrialised regions in the country producing considerable amounts of mineable copper, zinc, and gold due to mining activities. Development of metallurgical and mining industries has been increasing the pollution of surface waters by toxic chemical elements, particularly Cu, Mn, and Zn. We assessed the extent of these metal contaminations in surface waters in this region, by multivariate geostatistical analyses of the concentrations of the above-mentioned heavy metals over the five year periods (2017-2022). The dataset consists of element concentrations and sampling locations, for which it was provided by the Republican State Enterprise “Kazhydromet”. Principal Component Analysis (PCA) coupled with Simple Cokriging have been incorporated to characterise the local distribution of the heavy metals in surface waters in the East Kazakhstan region. The first component of PCA is used for further analysis since it qualifies for 74% of the total variation in the data. Then, a Simple Cokriging over the four continuous variables (PC1, Cu, Mn, and Zn) has been carried out to map their spatial distribution. Furthermore, the estimation map of PC1 is categorised, linking it to Cu, Mn, and Zn estimated maps; where the high, medium and low concentration areas of above-mentioned heavy metals are recognised over the entire region. The results are then interpreted by superimposing the river network into the estimation maps of elements.  

According to estimation maps, variations in concentrations of Cu, Mn, and Zn depend on the season and resulted in a distinct pattern. The surface waters in the region are mostly contaminated in spring and winter seasons due to snowfall and subsequent melting, whereas they are least contaminated during the summer and autumn. Moreover, it was observed that Cu shows the most mobility among the three toxic elements. A significant amount of Cu is discharged to the surface waters in Spring periodically, when snow melting activities are enhanced in Ust’-Kamenogorsk city, and transported to downstream regions. Therefore, higher concentrations of Cu near Semey city are observed during summer. The same effect has not been observed for Mn and Zn elements, which indicates that their overall mobility is lower.

Generally, observed Cu and Mn concentrations are exceeding the Maximum Allowable Concentration (MAC) by 5 times in the vicinity of Ust-Kamenogorsk and Ridder cities, while Zn exceeded its MAC by 10 times in the same region. One possible source of such high concentrations of heavy metals in this region is linked to the mining operations, especially to the Tailings Storage Facilities (TSF) that have been driving surface water contamination since the 1960s. After every relevant TSF has been superimposed on the estimated maps, the relationship between high metal concentration and TSF was investigated, leading to a conclusion that both active and closed Tailings Storage Facilities are the primary sources of surface water contamination in the study region. This shows that the situation of the study region in terms of surface water pollution is deplorable and needs urgent remediation actions.

Keywords: Multivariate Geostatistics, Water Contamination, Tailings Storage Facilities.

How to cite: Tileugabylov, A. and Madani, N.: Evaluation of Water Contamination in the East Kazakhstan Mining Area Using Multivariate Geostatistics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1737, https://doi.org/10.5194/egusphere-egu23-1737, 2023.

Determing soil spatial variability is a key point in soil sciences either for soil preparation in precision agriculture, or because of influence on overland flow and erosion. Soil Surface Roughness (SSR) represents the undulation of the surface at small scale, due to the presence of small elevations and depressions. It results from tillage operations and changes over time due to weathering. SSR can be related to clod-size distribution. So, many research has been conducted on monitoring the size and number of clods using photogrammetry method. Nowadays, it is possible to acquire high resolution Digital Elevation Models (DEMs). This study seeks to model the evolution of clod size under rainfall impact with modeling and data processing tools.

Seedbed-like soil surface was made in the laboratory by filling a tray with loose soil of silt loam and setting upon pre-sieved clods. It was eroded by controlled laboratory rainfalls. A millimeter DEM was recorded at each stage of the surface with laser-scanner. Wavelet-based clod segmentation leaded to measurement of the volume of individual clods. Clod subsets were formed according to clod size. Normalized mean volume decrease was modelled by exponential function.

Greater clods showed swelling (volume increase) and erosion (volume decrease), with cumulated rainfall. These two phenomena being size dependent. Amplitude and slope parameters of the exponential decrease of clod volume could be modelled as a function of mean volume of the clod subset at initial stage. Results obtained with this surface strengthen those previously obtained with less data and basic segmentation. A power law is confirmed for amplitude parameter and a sigmoïd function is highlighted for slope parameter.

Modelling and data processing tools are efficient to differentiate and estimate the dynamics of clods depending on their size. Usually, clod size distribution is addressed with statistics of clod diameters obtained by real or numerical sieving. Working on 2.5 D DEMs gives also an acces to the vertical dimension of clods, which is included in their volume. This technique completes the usual roughness description and is promissing for use in precision agriculture or numerical surface generation.

How to cite: Vannier, E. and Dusséaux, R.: Modeling clod evolution under rainfall according to clod size., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2761, https://doi.org/10.5194/egusphere-egu23-2761, 2023.

Land subsidence referring to the lowering of Earth’s land surface poses destructive threats to buildings and infrastructures and increases vulnerability to floods. The tendency to overexploit groundwater resources due to ever-increasing demand for water in urban areas is known as one of the main drivers for land subsidence, especially in regions with compressible sediments or formations susceptible to changes in groundwater pressure. Land subsidence has been observed in many countries around the globe including but not limited to USA, Mexico, Spain, Italy, Saudi Arabia, Iran, India, Vietnam, and China.

Artificial intelligence (AI) and machine learning algorithms prove to be of great values to assess and predict a variety of hydrological and environmental dynamics and trends (Hassani et al., 2021; Mahdaviara et al., 2022). Leveraging on this opportunity, we develop a new framework, assisted by AI approaches, to quantify and predict how various environmental and climatic parameters influence the occurrence and extent of land subsidence. We show the general applicability of the proposed framework through the case of land subsidence observed in Iran, i.e. a semi-arid to arid country which strongly relies on the limited groundwater resources for a wide range of activities. The country hosts some of the fastest-sinking cities in the world. As a case study, we focused on the land subsidence observed in Varamin plain located in central Iran with an average annual precipitation of 420 millimeters and 210 millimeters of subsidence per year in the last 20 years.  A combination of the field and satellite data over the last two decades was prepared for the training of the models. In the next level, the training matrix was exposed to the AI algorithms aiming to develop models relating the land subsidence rate to a variety of environmental and climatic factors. Our preliminary analysis suggests that the groundwater withdrawal and precipitation rate are among the most important parameters affecting the rate of subsidence. The modelling tools will be used to detect the potential hotspots for land subsidence under different water management and climate change scenarios in other places. This will be helpful for preventing the forthcoming damages and devising the necessary action plans to mitigate the situation under different conditions.

References

Hassani, A., Azapagic, A., Shokri, N. (2021). Global Predictions of Primary Soil Salinization Under Changing Climate in the 21st Century, Nat. Commun., 12, 6663. doi.org/10.1038/s41467-021-26907-3.

Mahdaviara, M., Sharifi, M., Bakhshian, B., Shokri, N. (2022), Prediction of Spontaneous Imbibition in Porous Media Using Deep and Ensemble Learning Techniques, Fuel, 329, 125349.

How to cite: Shokri, N., Mahdavi Ara, M., Ansari, S., and Sharifi, M.: Toward prediction of land subsidence assisted by artificial intelligence approaches, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5025, https://doi.org/10.5194/egusphere-egu23-5025, 2023.

We provide an extension of a well established geostatistical software to allow for effective and interactive assessment of environmental scenarios in a geostatistical context. The extension comprises a pre-built interface and a freely accessible demo application.

The heat of the approach relies on replacing a sample variogram with its uncertainty bound. Doing so enables one to fully and consistently embed various sources of uncertainties stemming from available datasets and methodological approaches employed for their interpretation. Methodological approaches included in the software include capabilities leading to: i) a statistical estimation of uncertainty bounds from residual point-pair distributions; ii) a statistical robustness test for uncertainty bounds; and iii) a Monte Carlo simulation tool to propagate a variety of aleatory uncertainties. 

We illustrate the capabilities of our approach and software through the analysis of two different datasets. We focus on manual variogram estimation to comprehensively illustrate how insights on uncertainty can be used to reject candidate variogram models or model parameter sets.

How to cite: Mälicke, M., Guadagnini, A., and Zehe, E.: SciKit-GStat Uncertainty: A software extension to cope with uncertain geostatistical estimates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6683, https://doi.org/10.5194/egusphere-egu23-6683, 2023.

Aquitards are common hydrogeological features in the subsurface and its properties are important for e.g. water resource management, subsidence, contamination transport and aquifer thermal energy storage. Typically pumping test are used to parameterize the hydraulic conductivity of aquitards. However, with analytical interpretation of pumping tests it is difficult to take spatial variability and uncertainty into account. Alternatively, core-scale measurements of hydraulic conductivity are used in geostatistical upscaling methods, for which their correlation lengths are needed. However, this information is extremely difficult to obtain. In this study we investigate whether a pumping test can be used to obtain the correlation lengths needed for geostatistical upscaling and  account for the uncertainty about heterogeneous aquitard conductivity. We generated random realizations from core scale data with varying correlation lengths and inserted these into a groundwater flow model which simulates the outcome of an actual pumping test. We selected the realizations which yielded a better fit to the pumping test data than the traditional pumping test result assuming homogeneous layers. Ranges of horizontal and vertical correlation lengths that fit the pumping test well are found. However, considerable uncertainty regarding the correlation lengths remains which should be considered when parameterizing a regional groundwater flow model.

How to cite: van Leer, M., Zaadnoordijk, W. J., Zech, A., Griffioen, J., and Bierkens, M.: Estimating correlation lengths of aquitard hydraulic conductivity by inverse geostatistical modelling of a pumping test, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8113, https://doi.org/10.5194/egusphere-egu23-8113, 2023.

Accurate and timely prediction of peak flow in streams is essential for transportation safety as these estimates can help transportation authorities implement precautionary measures (e.g., road closures, diversion, emergency routes, transportation planning, flood impact assessment, etc.) well ahead of time to mitigate the impacts of flooding on transportation. Often in practice, flow quantiles are estimated from catchment and climate attributes using simple methods such as linear regression, which overlooks the more complex nature of relationships between variables, potentially leading to errors and uncertainties in the estimates that can trickle down to engineering design. Here, we will discuss findings from our ongoing work on accurate estimation of peak flow using machine learning algorithms. The methodology involves a two-step process. First, k-means clustering is implemented to identify regions that have similarities in the mean annual runoff. Second, Random Forest is implemented to map a wide range of climate and catchment features to flow quantiles in each cluster. To assess the effectiveness of this approach in increasing transportation resilience, we will show how the peak flow estimates from this new approach compare with the estimates from the existing approach followed by the Nebraska Department of Transportation and explore the potential of these new estimates to be used for operational purposes for flood-related decision-making in the context to transportation infrastructure.

How to cite: Pokharel, S., Roy, T., and Admiraal, D.: Machine learning-based peak flow estimation for improved flood resilience of transportation infrastructure, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10053, https://doi.org/10.5194/egusphere-egu23-10053, 2023.

In environmental sciences, it is common to collect and analyze spatio-temporal multivariate data concerning several variables which are measured in time over a spatial domain. The spatio-temporal data are usually sparce in space, due to the high cost of the equipment, and temporal dense since the required variables are regularly sampled in time.

In the literature different methods have been proposed for the analysis of such spatio-temporal data which exhibit a correlation in space and time as well as in-between variables. Among them it is worth recalling the generalization of Blind Source Separation technique for multivariate space-time random field (stBSS) and the space-time linear coregionalization model (ST-LCM). These methods are useful to simplify the spatio-temporal multivariate analysis since by a linear transformation of the original observations only the independent components which exhibit a spatio-temporal correlation are retained (lower than the number of observed variables) and modelled.

In this paper a multivariate study regarding seven environmental variables (evapotranspiration level, minimum and maximum temperature, minimum and maximum humidity, wind speed and precipitation) measured between 2000 and 2022 in Veneto region (Italy) will be proposed.  Both the stBSS and the joint diagonalization of the empirical covariance matrix approach will be used to identify the hidden components, and properly chosen spatio-temporal models will be fitted to the latent components. Note that for the first approach a BSS model for the multivariate random field will be assumed, whereas for second one a space-time linear coregionalization model (ST-LCM) for the independent components will be fitted to the matrix-valued covariance function estimated for seven relevant environmental variables.

Finally, the fitted models have been used to predict evapotranspiration levels and a comparison of the values obtained by using the two different techniques will be provided.

How to cite: Cappello, C., De Iaco, S., Palma, M., Muehlmann, C., and Nordhausen, K.: Space-time multivariate techniques: a comparative analysis on environmental data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16194, https://doi.org/10.5194/egusphere-egu23-16194, 2023.