Forschungszentrum Landschaftsentwicklung und Bergbaulandschaften (FZLB)
Loop-loop electromagnetic induction (EMI) has proven to be efficient for fast and real-time soil apparent electrical conductivity (ECa) measurements. It is important to develop robust and accurate inversion strategies to obtain
soil electromagnetic conductivity image (EMCI) from ECa data. Moreover, obtaining an accurate nonlinear relationship between subsurface electrical conductivity (σ) and water content (θ) plays a key role for soil moisture monitoring using EMI. Here, we incorporated probabilistic inversion of multi-configuration ECa data with dimensionality reduction technique through the discrete cosine transform (DCT) using training image (TI)-based parametrization to retrieve soil EMCI. The ECa data were measured repeatedly along a 10 m transect using a CMD mini-Explorer sensor. Time-lapse reference data were collected as well to benchmark the inversion results and to find the in-situ relationship between σ and θ. To convert the inversely estimated timelapse EMCI to the soil moisture,we examined two approaches, namely, Rhoades et al. (1976) model and artificial neural network (ANN). The proposed inversion strategy estimated the soil EMCI with an excellent agreement with the reference counterpart. Moreover, the ANN approach demonstrated superiorities than the commonly used petrophysical model of Rhoades et al. (1976) to obtain spatiotemporal images of θ from time-lapse EMCI.
The results demonstrated that incorporation of the DCT-based probabilistic inversion of ECa data with the ANN approach offers a great promise for accurate characterization of the temporal wetting front and root zone soil moisture.
Low frequency loop-loop electromagnetic induction (EMI) is a widely-used geophysical measurement method to rapidly measure in situ the apparent electrical conductivity (ECa) of variably-saturated soils. Here, we couple Bayesian inversion of a quasi-two-dimensional electromagnetic (EM) model with image compression via the discrete cosine transform (DCT) for subsurface electrical conductivity (EC) imaging. The subsurface EC distributions are obtained from multi-configuration EMI data measured with a CMD-Explorer sensor along two transects in the Chicken Creek catchment (Brandenburg, Germany). Dipole-dipole electrical resistivity tomography (ERT) data are used to benchmark the inferred EC fields of both transects. We are especially concerned with the impact of the DCT truncation method on the accuracy and reliability of the inversely-estimated EC images. We contrast the results of two different truncation approaches for
model parametrization. The first scenario considers an arbitrary selection of the dominant DCT coefficients and their prior distributions (a commonly-used approach), while the second methodology benefits from geostatistical simulation of the EMI data pseudosection. This study demonstrates that DCT truncation based on geostatistical simulations facilitates a robust selection of the dominant DCT coefficients and their prior ranges, resulting in more accurate subsurface EC imaging from multi-configuration EMI data. Results based on geostatistical prior modeling present an excellent agreement between the EMI- and ERT-derived EC fields of the Chicken Creek catchment.
Exploring hydrological and ecological processes plays a key role in understanding ecosystem developments. In this respect, the constructed catchment, Chicken Creek, has been established for fundamental and interdisciplinary scientific research. Since 2005, an ongoing monitoring program has been launched to measure hydrological, biological, meteorological, and pedological parameters during the ecological development of the site. This comprehensive and multidisciplinary monitoring program has produced a diverse large data set. Handling such complex data for research purposes can be a cumbersome task. Consequently, we developed an online data portal (https://www.b-tu.de/chicken-creek/apps/datenportal/) to efficiently handle the data from Chicken Creek catchment. The portal was constructed using Shiny package of the R programming language. This platform provides a web-based data repository allowing for data discovery, download, visualization, and analysis. The data include time series of different parameters from installed sensors, data from laboratory analyses, vegetation data, data from sampling campaigns, and aerial photos. This platform demonstrates the relevancy and potentiality of the R-Shiny for constructing an online data portal to be used for multidisciplinary scientific purposes. The Chicken Creek
data portal thus provides a comprehensive and reliable database to give scientists a fast and easy access to all collected data.
Exploring hydrological and ecological processes plays a key role in understanding ecosystem development. In this respect, the constructed catchment, Chicken Creek (Hühnerwasser), has been established to promote fundamental and interdisciplinary scientific research. Since 2005, an on-going monitoring program has been established in this catchment to measure hydrological, biological, meteorological, and pedological parameters during the ecological development of the site. This comprehensive and multidisciplinary monitoring program has produced a large and diverse data set. Managing and exploring such a complex data set for research purposes can be a cumbersome task. As a consequence, we developed an online data portal https://www.b-tu.de/chicken-creek/apps/datenportal/ to efficiently handle the data from Chicken Creek catchment. The portal was constructed using R programming language, Shiny package, and the accompanying local Linux server. This platform allows for efficient data discovery, download, visualization, and analysis. Data visualizations are freely available, while data storage is limited to the authorized users. In comparison with the commonly used data base tools, R-Shiny offers several advantages. As an open source package, this platform allows for constructing a web data platform in an interactive way taking into account the complexity and diversity of the data. The flexibility of this routine enables to make an efficient user-demand data portal rather than relying on predetermined outputs. It also offers flexibility in data handling by using many different R packages, rendering versatile and extensive functionalities. R-Shiny is particularly powerful in terms of statistical analyses. Although the Chicken Creek online data portal is complete and available, new features, and extended capabilities are under active development. The Chicken Creek data portal provides a comprehensive and reliable database to give scientists a fast and easy access to all collected data.
Consequently, R-Shiny offers a great potential for future development of the web-based data portals to efficiently handle scientific data collected at different scales.
The Huehnerwasser catchment was constructed 2004-2005 in a post-mining landscape in Eastern Germany and was left for an unmanaged primary ecosystem succession. At the same time a comprehensive monitoring program was launched for observing the expected dynamic development of this 6 ha site. During the following years this artificially created system was subject to a series of fast changes with regard to morphology, hydrology or vegetation cover. In summary, these processes resulted in significant alterations of the original, initial system within a short period of time. This development can be divided up into single phases with dominating processes or groups of processes. A first phase was mainly characterized by interactions between abiotic system components such as water and substrate. However, already in this very early developmental phase biotic components in the form of microorganisms were a crucial system forming factor by means of biological soil crusts. This period was quickly replaced by a second phase with dominating hydrological processes. During this phase the local groundwater body of the system developed to its full extent. With the beginning of the present phase biota gained control of the system behavior. Particularly plants and related processes such as water uptake and evapotranspiration are now responsible for new modifications of the system. The Huehnerwasser site offers an example of young and highly dynamic ecological systems. This transitional development of a very young ecosystem contrasts with the behavior of mature ecosystems with slow and less pronounced changes unless the system is disturbed. In this paper we present insights into a unique landscape observatory which illustrates the dynamic nature of natural systems in fast motion.
Knowledge of catchment 3D spatial heterogeneity is crucial for the assessment and modeling of eco-hydrological processes. Especially during the initial development phase of a hydro-geo-system, the primary structural properties have the potential to determine further development pathways. Small-scale heterogeneity (cm to m scale) may have significant effects on processes on larger spatial scales, but is difficult to measure and quantify. The Hühnerwasser (Chicken Creek) catchment offers the unique opportunity to study early ecosystem development within an initial structural setup that is well-known, from the plot up to the catchment scale.
Based on information on the open-cast mining technology, catchment boundaries and sediment properties, we developed a structure generator program for the process-based modeling of specific dumping structures and sediment property distributions on the catchment. The structure generator reproduces the trajectories of spoil ridges and can be conditioned to reproduce actual sediment distributions according to remote sensing and soil sampling data. Alternatively, sediment distribution scenarios can be generated based on geological data from the excavation site, or can be distributed stochastically. Using pedotransfer functions, the effective hydraulic van-Genuchten parameters are then calculated from sediment texture and bulk density. The main application of the 3D catchment model is to provide detailed 3D-distributed flow domain information for hydrological flow modeling. Observation data are available from catchment monitoring are available for determining the boundary conditions (e.g., precipitation), and the calibration / validation of the model (catchment discharge, ground water). The analysis of multiple sediment distribution scenarios allows to evaluate the effect of initial conditions on hydrological behavior development. Generally, the modeling approach can be used to pinpoint the influx of specific soil structural features on ecohydrological processes across spatial scales.
Ecosystems are characterized by high complexity and are dynamically changing by abiotic and biotic drivers such as climate, vegetation and soil fauna. Due to feedback processes between compartments, ecosystems show an inherent degree of locally heterogeneous properties and structures at multiple spatial and temporal scales.
The constructed catchment “Chicken Creek” represents a unique and outstanding site to study an ecosystem at the initial stage of development. The catchment is located about 20 km south of the city of Cottbus (Germany). The 6-ha catchment area was built in 2005 of coarse-textured quaternary sediments from the adjacent lignite mine and forms a back- and foot-slope that flattens out to a pond. A clay liner of 2-3 m thickness seals the catchment at the bottom, allowing for the formation of a local water body fed by precipitation. After construction the site was left to an unrestricted and unmanaged succession to elucidate the role of structures und processes during the initial establishment of an ecosystem. To study processes of ecosystem development and newly emerging structures the Chicken Creek catchment is equipped with a comprehensive ecological monitoring network. A network of more than 40 wells and two weirs allow for recording groundwater levels, surface runoff and discharge. Meteorological parameters are recorded at three weather stations. Annual aerial photos and vegetation monitoring reveal the floral colonisation of the area. Here, we present the 12 years of diverse and extensive monitoring program which has been launched to measure hydrological, biological, meteorological, and pedological parameters during the ecological development of the catchment area.
After completion of the construction in 2005 (Gerwin et al. 2009), a major challenge was to develop and install a cross-disciplinary long-term monitoring program for the 6 ha area to record major environmental parameters adapted to the development of the site (Schaaf et al. 2013).
During its first 15 years, Chicken Creek showed a very dynamic development (Elmer et al 2013). Whereas the abiotic geosystem of the first 2-3 years was characterized by heavy erosion and sediment transport, primary succession by invading vegetation and the unexpected formation of soil crusts within few years resulted in more biotic-abiotic feedbacks that controlled catchment hydrology. Our observations over a period of 15 years indicate that even minor variations in initial substrate characteristics (e.g. texture) can have lasting impacts on geomorphical, hydrological and biological development like erosion intensity, groundwater levels or establishment of vegetation patterns. The time-series of monitoring data combined with a structure model of the catchment (Gerke et al. 2013) allowed the closure of the water balance by relatively simple calculations of water storage volumes and the estimation of evapotranspiration (Schaaf et al. 2017). From these data three stages of ecosystem development were derived which are characterized by increasing influence of biota and increasing system complexity.
Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity
(2019)
Soil water content (θ) is a key variable in different earth science disciplines since it mediates the water and energy exchange between the surface and atmosphere. Electrical and electromagnetic geophysical techniques have been widely used to estimate soil electrical conductivity (σ) and soil moisture. However, obtaining the σ − θ relationship is not straightforward due to the non-linearity and also dependency on many different soil and environmental properties. The purpose of this paper is to determine if artificial neural network is an appropriate machine learning technique for relating electrical conductivity to soil water content. In this respect, time-lapse electrical resistivity tomography measurements were carried out along a transect in the Chicken Creek catchment (Brandenburg, Germany). To ensure proper retrieval of the σ and θ, reference values were measured near the beginning of the transect via an excavated pit using 5TE capacitance sensors installed at different depths. We explored robustness and pertinence of the artificial neural network approach in comparison with Rhoades model (as a commonly used petrophysical relationship) to convert the inversely estimated σ from electrical resistivity tomography to the θ. The proposed approach was successfully validated and benchmarked by comparing the estimated values with the reference data. This study showed the superiority of the artificial neural network approach to the Rhoades model to obtain σ − θ relationship. In particular, artificial neural network allowed for more accurate estimation of the temporal wetting front than the petrophysical model. The proposed methodology thus offers a great promise for deriving spatiotemporal soil moisture patterns from geophysical data and obtaining the in situ σ − θ relationship, taking into account the non-linear variations of the soil moisture.
Low-frequency loop–loop electromagnetic induction (EMI) offers several key advantages over many other geophysical techniques for proximal soil sensing. Yet, because of problems with the inversion of measured apparent electrical conductivity (ECa) data, application of EMI for geophysical imaging and interpretation is limited. In this study, a Bayesian inference was used to obtain electromagnetic conductivity images (EMCIs) from multiconfiguration ECa data. This approach allows analysis of highly nonlinear problems and renders an ensemble of models obtained from the posterior distribution that can be used to explore parameter uncertainty. In this respect, generalized formal likelihood function was used to more accurately describe the sensitivity of the posterior distribution to residual assumptions. Discrete cosine transform (DCT) was employed as a model compression technique to reduce the number of unknown parameters in the inversion. The DCT parameterization was performed using training image (TI)-based geostatistical simulations considering the ECa data pseudosection as a TI. The potential of the proposed approach was examined through different theoretical scenarios. The estimated subsurface EMCI shows excellent agreement with the original synthetic models subject to the appropriate choice of prior information. Moreover, DCT parameterization reduces the number of unknown parameters, increasing accuracy of the inversion with the Bayesian procedure. The proposed approach ensures accurate and high-resolution characterization of subsurface conductivity layering from measured ECa values.