HS8.3.1

Heterogeneity plays a major role in subsurface processes from the local scale (preferential infiltration and flow paths, fractures) to the catchment scale (presence of lateral and vertical variability, multiple horizons, bedrock interface, etc.). If high-resolution direct observations are often available through drillholes, CPT or installing in-situ monitoring probes, those local measurements only provide punctual or 1D information. Within this context, geophysical techniques can provide relevant spatially-distributed information (2D, 3D or even 4D) with a much larger coverage than direct measurements. However, geophysical information remains indirect and must be translated into the sought parameter through petrophysical or transfer functions. 

Geophysicists are facing two important issues when imaging the subsurface: 1) Generating images of the subsurface that are consistent in terms of soil or geological structures; 2) Integrating the geophysical information into hydrological models. Both issues will be discussed in this contribution.

Geophysical imaging is the result of an inversion process whose solution is non-unique. This problem is generally solved using a regularization approach introducing some a priori characteristics of the model. The dominant choice is still the smoothness constraint inversion, which often introduces a too simplistic representation of the subsurface, and decreases the potential of geophysics to discriminate between different facies. In the first part of this contribution, we will analyze what can be expected from geophysical methods in terms of characterization of the heterogeneity. We will illustrate how the inversion method affects the discrimination potential of geophysics, and how we can improve the geophysical image by accounting for prior information. We will see how the discrimination potential decreases with the loss of resolution. Finally, we will investigate how recent methodologies using machine learning can improve our ability to image the subsurface.

Given the high spatial coverage of geophysical methods, they have a huge potential to inform hydrological models in terms of heterogeneity. However, the limitations related to geophysical inversion also make the geophysical model uncertain and the risk to propagate erroneous information exists. In the second part of this contribution, we will illustrate how to incorporate geophysical data into hydrological models to unravel their spatial complexity. At the early stage of a project, several scenarios regarding spatial heterogeneity are often possible (orientation of fractures, number of facies to consider, interconnection within one facies, etc.), and this can largely influence the outcomes of the hydrological models. In this context, geophysical data can be used to verify the consistency of some scenarios without requiring any inversion in a process called falsification. Once realistic scenarios have been identified, geophysical data can be used to spatially constrain hydrological models. However, this should ideally account for the uncertainty related to geophysical inversion. One possibility is to use a fully-coupled approach where geophysical data are integrated directly in the hydrological model inversion. This requires nevertheless a transfer function to relate hydrological and geophysical variables. As an alternative, a sequential approach using a probabilistic framework accounting for the imperfect geophysical data can be used. The latter requires co-located measurements.

How to cite: Hermans, T., Michel, H., Lopez-Alvis, J., and Nguyen, F.: Imaging the subsurface to inform hydrological models: a geophysicist’s perspective, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-319, https://doi.org/10.5194/egusphere-egu21-319, 2021.

One of the major challenges in soil hydrological modelling is due to the fact that soils are heterogeneous at all spatial scales. The identification and accurate representation of such heterogeneity can be crucial for quantifying the subsurface hydrological states and water fluxes.

This work presents the results of an integrated approach for process-based soil hydrological modelling for a highly instrumented hillslope site. The approach builds on the integration of classical soil mapping, on accurate monitoring of soil water dynamics as well as on geophysical measurements for characterising subsurface heterogeneity. It finally integrates the gathered information into a physical model for simulating the soil water dynamics with high spatial and temporal resolution.

At the Schäfertal Hillslope site (Central Germany), the soil monitoring network STH-net provides high-quality data about the soil water dynamics and soil properties at 8 instrumented soil profiles and depths within the unsaturated zone. The soil spatial variability, known from local soil description and sampling, was mapped using time-lapse electromagnetic induction measurements. The geophysical inversion of the data provided depth-resolved information about the subsurface structures in terms of soil-bedrock interface, soil horizons and their spatial continuity along the hillslope transect. Based on this, different versions of the subsurface geometry model were produced and associated to soil hydraulic parameterizations derived from different approaches.

We show the performance of the physical model CRITERIA-3D in reproducing the soil water dynamics for different subsurface models with increasing complexity. Specifically, we highlight and discuss the key challenges that need to be addressed when continuous information about the subsurface heterogeneity is to be mapped in the field with high resolution and represented in a numerical model with fine discretization in three-dimensions.

We conclude that linking state-of-the-art experimental methods to advanced numerical tools, and bridging the gap between different disciplines such as pedology, hydrology and geophysics can be the key for improving our ability to measure, predict and better understand the vadose-zone processes. This will provide important knowledge needed for transferring this approach to larger scales where the accurate quantification of the soil water fluxes is required for a more efficient water management in the context of sustainable food production and climate change.

How to cite: Martini, E., Wollschläger, U., Bittelli, M., Tomei, F., Werban, U., Zacharias, S., and Roth, K.: Hillslope-scale mapping and numerical representation of the subsurface heterogeneity towards an improved process-based hydrological modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2848, https://doi.org/10.5194/egusphere-egu21-2848, 2021.

How to cite: Riedel, L., Bauser, H. H., Maiwald, R., and Ospina De Los Ríos, S.: Predicting Solute Transport in Soil Water Flow with Estimated, Effective Material Properties, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9954, https://doi.org/10.5194/egusphere-egu21-9954, 2021.

Artificial recharge is the set of techniques used to increase or facilitate the flow of water to aquifers. It has been a management strategy for centuries to optimize the use of water in regions where the seasonal or inter-annual distribution of surface water produces periods or exceedance and shortage. Water infiltration into aquifers is enhanced such that aquifers serve as short to medium term storage reservoirs. Water is recovered when needed.  Recently, increasing demand of groundwater and the occurrence of more severe and longer droughts in different regions around the world have produced a renewed interest in the application of this management strategy in many countries, particularly in arid and semi-arid regions.

Infiltration wells are a common method to apply artificial recharge, which allows infiltrating water directly into saturated aquifers or to the unsaturated zone. We performed local-scale numerical simulations of unsaturated flow to model the operation of a single well infiltration system. Based on the results of the simulations, we evaluate its performance considering different conceptualizations of the materials present in the vadose zone. We conclude that the performance of similar systems can be significantly different depending on the distribution of subsurface materials and their properties. Hence, the conceptualization and modeling of such systems require some care about how the inherent heterogeneity of aquifers is included in models.  Last, we provide some recommendations for the design and assessment of similar artificial recharge systems. 

How to cite: Herrera, P. and Olivares, Y.: Assessment of an artificial recharge system through numerical simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10013, https://doi.org/10.5194/egusphere-egu21-10013, 2021.

Understanding and predicting the macro-scale flow characteristics in the fractured vadose zone is of great importance for subsurface hydrological applications. Here we develop a network model to study infiltration in unsaturated fracture networks. We consider an idealized honeycomb-like fracture network composed of a series of Y-shaped and inverted Y-shaped intersections. At the scale of intersections, liquid storage/release and splitting/convergence behaviors are modeled according to local splitting relationships obtained from detailed laboratory work and numerical simulations. By varying the splitting relationships, the influence of local flow behaviors on large scale flow structures is systematically investigated. We find that when the water split tends to split equally at the intersection, a divergent flow structure forms in the network. Conversely, unequal splitting leads to preferential pathways. We also find that an avalanche infiltration mode, i.e., sudden release of a large amount of water from the network, emerges spontaneously, and is modulated by the local splitting behavior. The pathways of preferential flow is controlled by the liquid volume triggered by avalanches and the network structure. The improved understanding from this study may shed new light on the complex flow dynamics for unsaturated flow in fractured media.

How to cite: Xue, S., Yang, Z., and Chen, Y.-F.: Influence of local splitting behavior at intersections on infiltration in an unsaturated fracture network , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3663, https://doi.org/10.5194/egusphere-egu21-3663, 2021.

A fundamental understanding of water infiltration in unsaturated fractured rocks is important in a range of subsurface hydrological, environmental and engineering applications. We perform an experimental and modeling investigation of the gravity-driven liquid slug flow behavior at fracture intersections. In the experiments, we visualize the flow processes and quantitatively analyze the flow dynamics. We develop a novel computational model that adequately captures the splitting dynamics. This model considers dynamic contact angles and solves temporal evolution of interface motion based on force balance with the quasi-static assumption at each time step. We systematically examine the influence of various physical parameters on the flow splitting behavior, including the widths and inclination angles of channels, and slug lengths. Using the local splitting relationships obtained mechanistically, develop a network model to study infiltration in unsaturated fracture networks. Then, the influence of the local flow dynamics on large-scale flow structures is systematically investigated. We find that an avalanche infiltration mode emerges spontaneously and that the local splitting relationship controls the divergent and convergent flow structures.

How to cite: Yang, Z., Xue, S., Hu, R., and Chen, Y.-F.: Gravity-driven liquid splitting behavior at intersections and its control on infiltration in unsaturated fracture networks , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8582, https://doi.org/10.5194/egusphere-egu21-8582, 2021.

One third of the caves in the north-eastern part of the Eastern Alps are assumed to be created by frost weathering. The geomorphological process of frost weathering is linked to temperature variations around the freezing point and a sufficient amount of water in the inner of a rock. Fractured areas are highly sensitive to frost weathering and are characterised by high variations of temperature and water content. Geophysical electrical methods are widely used to monitor variations of temperature and water content with time, considering the sensitivity of the electrical resistivity to both properties. In this study, we present imaging results for electrical monitoring conducted in February 2020 in the ceiling of the Untere Traisenbacherhöhle, a frost weathering cave, which is located in the foothills of the Eastern Calcareous Alps. In total, 77 imaging measurements were conducted during the monitoring period of approximately 60 hours with an electrode separation of about 10 cm to gain data with high temporal and high spatial resolution during and after a raining event. Simultaneously, temperature was measured at one point in different rock depths. Geophysical data was pre-processed by a four-step filtering procedure to identify and remove spatial and temporal outliers. Then the data was inverted with the open-source library Pybert. Inversion results reveal that during the entire monitoring the resistivity varies up to ±30% compared with the values at the start of the monitoring. To investigate in more detail the temporal changes, we extracted pixel values in 16 areas. These pixels show a strong negative linear correlation with the temperature (correlation coefficients up to 99%), which ranges between 2 °C and 8 °C. However, in some areas a simple linear model seems to not represent the relationship of both parameters in the low temperature range adequately. Based on such correlation, the resistivity data was temperature-corrected to investigate water content changes affecting the resistivity of the ceiling of the cave. Such analysis permitted to delineate clear anomalies related to water seeping into the rock as well as drying processes at the inner parts of the rock wall. Further, geophysical measurements were conducted by means of the low-induction number electromagnetic method in June 2020 to evaluate the applicability of this to map the entire rock wall of the cave. Electrical resistivity (ERT) data differs strongly from the low-induction number electromagnetic (EMI) mapping, likely because of a contamination of the EMI data due to the presence of the metallic electrodes used for the ERT monitoring and due to different weather conditions. Our study reveals the possibility to quantify water content changes in caves in an imaging framework. Further, this information can be used to delineate fractured zones in carbonate rocks, which are supposed to be more sensitive to frost weathering.

How to cite: Moser, C., Oberender, P., Funk, B., and Flores-Orozco, A.: Geophysical monitoring of temperature variation and water movement in a frost weathering cave, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13388, https://doi.org/10.5194/egusphere-egu21-13388, 2021.

Nutrient losses in agricultural areas have detrimental effects not only on the surface water quality but are also unfavorable for sustainable agriculture practices. In Denmark, there are currently nitrate regulations applied for ID15 catchments (15 square km scale), nevertheless, it is crucial to know how much nitrogen is retained in the root zone, saturated zone, riparian zone as N-retention varies widely within ID15 catchments. Currently, N-retention mapping does not incorporate N-retention in the root zone on ID15 scale. N-retention in the root zone of subsurface drained clayey areas is potentially influenced by the variation in water table depth. Therefore, we will evaluate the effect of shallow hydrogeology, topography and drain parameters on local (sub-field scale) water table depth variation using a case study in eastern Jutland, Denmark.

The aim of the study was to assess which hydrogeological variables, drain parameters and topographical variables control water table depth variation in the root zone. This analysis was aided by a groundwater flow model code (MODFLOW). For the following purpose, hydrological data (drain flow at the outlet and depth to the water table in piezometers) and geophysical data (subsurface electrical conductivity) were collected. The geophysical data was collected by two ground-based electromagnetic systems (DUALEM and tTEM). The electrical conductivities were directly translated into two zones of homogeneous hydraulic conductivities based on a threshold value. Hydraulic properties were varied for each zone. Areas with no geophysical data were simulated using Direct Sampling, a Multi Points Statistics method. We generated several flow models, which had a varying spatial distribution of hydraulic zones and varying hydraulic properties (input factors). Moreover, boundary conditions (lateral fluxes), topographical smoothing and drain parameters (drain conductance and drain depths) were some of the other input factors we considered in this work. Model boundary conditions data were obtained from the national hydrological model. The variation in input factors was related to variation in simulated water table depths and drain flow at the outlet using a one-at-a-time sensitivity analysis.

Drain flow fraction, depth to the water table and drain discharge are analyzed as the quantity of interest for both wet and dry periods. Drain fraction is calculated as the ratio of the area contributing to the drainage to the area contributing to the recharge within the same area. The results will discover crucial controlling components of water table depth with which variations in N-retention can be estimated between different fields. The emphasis is to discover the connection between hydrogeological, topographical, and drain variables, and water table depth. We will examine potential implications for evaluating drain fraction and potential nitrate reduction.

How to cite: Mahmood, H. and Rumph Frederiksen, R.: Hydrogeological, topographical and drain factors controlling water table depth variation and potential nitrate reduction in subsurface drained clayey till area, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5960, https://doi.org/10.5194/egusphere-egu21-5960, 2021.