Forschungszentrum Landschaftsentwicklung und Bergbaulandschaften (FZLB)
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The German government's decision to phase out lignite mining by 2038 or earlier, as recently 2030 has been agreed for the Rhineland, will trigger a number of transition processes in Germany's remaining lignite mining areas. The two largest lignite mining areas are located in geographically different regions: Rhineland in the west and Lusatia in the east. As the socio-economic and environmental conditions in these two mining areas are set to change dramatically, the German government has adopted extensive economic support measures. However, the environmental changes will also lead to changes in the ecosystem functions and services provided by the future post-mining landscapes.
Gerwin et al. (2023) compare the two main lignite producing regions of Germany in terms of their natural and cultural environments. The economic situation and its history are reflected and differences are outlined. Part of the differences in cultural development can be explained by the natural conditions, especially the edaphic factors and the climatic situation. Because of the specific geological settings, tailored mining technologies were developed and used in the two regions, with different effects on the resulting post-mining landscapes.
We conclude that the landscapes of Lusatia and the Rhineland have been radically restructured by the long and varied history of lignite mining. Both regions will change significantly as the mining industry continues to decline and is expected to cease altogether within the next decade. These changes in the post-mining landscapes and the ecosystem services will provide both challenges and opportunities. The preconditions for positive socio-economic development and for sustainable land use concepts that also consider ecological aspects are different for the two regions. The exchange of knowledge and experience between the two mining regions is crucial to the success of this major transformation process, despite, or perhaps because of, these differences.
Gerwin, W., Raab, T., Birkhofer, K., Hinz, C., Letmathe, P., Leuchner, M., Roß-Nickoll, M., Rüde, T., Trachte, K., Wätzold, F., Lehmkuhl, F. (2023): Perspectives of lignite post-mining landscapes under changing environmental conditions: what can we learn from a comparison between the Rhenish and Lusatian region in Germany? Environmental Sciences Europe 35:36. https://doi.org/10.1186/s12302-023-00738-z
Severe disturbances of landscapes entail an ecosystem development with the formation of structures and functions which may reach either a new equilibrium state or a state similar to the original ecosystem. Natural disturbances can result from major events such as volcanoes, glaciers, or denudations from landslides. Major disturbance may also evolve from anthropogenic influences such as from mining operations. They all can be considered starting points for the development of ecosystems from ‘point zero’, which was one central research interest of Dieter Mueller-Dombois. In this paper results from research in the Lusatian post-mining landscapes (Eastern Germany) are presented. Different methodological approaches are discussed. The well-defined ‘point zero’ of the ecosystem development allows for research on chronosequence designs as well as real time series studies. Chronosequences have been investigated to gain insight into the medium to long-term direction of the development. Real time series are recorded to obtain a more detailed understanding. The paper is structured into three main parts: First, effects of ecosystem disturbances by mining in Lusatia and the initial conditions for restoration are presented. In the following part practical rehabilitation measures and land use options are discussed. Finally, the third part summarizes results of long-term monitoring in an artificial watershed. In conclusion, post-mining landscapes allow for relevant case studies of ecosystem development after severe disturbances. Particularly, the starting point of the initial phase is very well defined which distinguishes these anthropogenically disturbed landscapes from landscapes after natural disturbances.
Charakterisierung und Stabilität organo-mineralischer Bodenhilfsstoffe aus Eisenhydroxidschlämmen
(2023)
Background The decision of the German federal government to cease lignite mining until 2038 or—if possible— already earlier until 2030, will cause manifold transition processes in the remaining lignite mining districts of Germany. The two largest districts are located in geographically opposite regions: The Rhineland in the western part and Lusatia in the east of Germany. As particularly these two mining districts will experience severe changes in their socioeconomic as well environmental conditions, the federal government has adopted comprehensive economic support measures. However, the environmental changes will also cause altered ecosystem functions and services to be provided by the future post-mining landscapes.
Results In this paper, the two main lignite-producing regions of Germany are compared with regard to their natural and cultural settings. The economic situation and its history are reflected and differences are outlined. Part of the disparities in the cultural development can be explained by very different natural conditions, especially edaphic factors and climatic situation. Because of dissimilar geological settings, different mining technologies were developed and are in use in the two regions with distinct effects on the resulting post-mining landscapes.
Conclusion The long-standing and manifold lignite mining activities have radically restructured the landscapes in Lusatia and the Rhineland. With the ongoing decline of the mining industry and its complete cessation, presumably within the next decade, both regions will alter their faces significantly. These changes offer both challenges but also opportunities with respect to the post-mining landscapes and their ecosystem services they are going to provide. The prerequisites for a positive socioeconomic development and for sustainable land-use concepts that also consider ecological aspects are different for both regions. However, or especially because of these differences, the knowledge exchange and experience transfer between both mining regions are pivotal for the success of this extensive transformation process.
Die zurückliegenden trocken-heißen Sommer haben im Süden Brandenburgs zu zahlreichen Bränden in den dominierenden Kiefernwäldern geführt, einige davon mit katastrophalen Ausmaßen. Dazu gehören die extremen Brandereignisse 2018 bei Treuenbrietzen sowie 2019 bei Jüterbog und Lieberose, die zusammen weit über 1000 ha Waldfläche zerstört und dabei auch Veränderungen der betroffenen Waldböden verursacht haben.
Das seit Mai 2020 von der FNR geförderte Verbundvorhaben Pyrophob befasst sich mit den Auswirkungen von Waldbränden im Süden Brandenburgs auf die Waldökosysteme und mit waldbaulichen Maßnahmen, die mittelfristig zu einer Verringerung der Brandanfälligkeit der Wälder beitragen sollen. Im Rahmen des auf eine fünfjährige Laufzeit ausgelegten Verbundvorhabens finden bodenkundliche Untersuchungen auf zwölf Versuchsflächen bei Treuenbrietzen und Jüterbog statt. An beiden Standorten werden unverbrannte Referenzflächen mit einbezogen. Ergänzend konnten bei Lieberose bereits 2019 nur kurz nach dem Brandgeschehen erste Bodenuntersuchungen durchgeführt werden. Die untersuchten Brandflächen bei Jüterbog und Lieberose sind als Totalreservate ausgewiesen, auf denen auch nach dem Brand waldbauliche Maßnahmen vollständig unterbleiben. Auf den Flächen bei Treuenbrietzen wurden verschiedene Behandlungsvarianten durchgeführt, zu denen auf einem Teil der Flächen die vollständige Entfernung der abgestorbenen Bäume gehörte, während auf anderen Teilflächen Totholz auf der Fläche verblieb.
Ziel der bodenkundlichen Arbeiten in Pyrophob ist zum einen eine Charakterisierung der direkten Auswirkungen des Brandes auf Humus und Mineralboden hinsichtlich des Stoffbestandes und bodenbiologischer Prozesse. Zum anderen wird der Stoffaustrag aus dem Oberboden erfasst. Der etwaige Einfluss der unterschiedlichen waldbaulichen Vorgehensweise auf die Bodenverhältnisse wird dabei als ein Indikator für die Auswahl geeigneter Maßnahmen berücksichtigt.
In den verschiedenen Untersuchungsvarianten des Vorhabens wurden zu diesem Zweck Humus- und Mineralbodenprofile untersucht. Zudem wurden Mischproben aus dem Ober- und Unterboden entnommen und auf ihren Stoffhaushalt hin analysiert. Auf jeder Untersuchungsfläche wurden je drei frei dränende Kleinlysimeter in 10 cm Bodentiefe installiert, um die zeitlichen Veränderungen der Sickerwasserzusammensetzung und die durch den Brand initiierten Stoffausträge aus dem Oberboden zu quantifizieren. Ergänzend finden Untersuchungen zu möglichen Auswirkungen der Brände auf bodenbiologische Prozesse wie Streuabbau und Stickstoffmineralisation statt.
Die Lausitz zählt schon heute zu einer der niederschlagärmsten Regionen in Deutschland. Durch den Klimawandel wird diese Situation noch weiter verstärkt und die Wahrscheinlichkeit von extremen Trockenperioden während der Hauptvegetationsphase wird zunehmen. Einer der zentralen bodenkundlichen Herausforderungen wird es somit sein, die geringe Wasserhaltekapazität der vor allem sandigen Böden der Region zu steigern. In dem Verbundvorhaben StabilOrg wird daher die Entwicklung neuartiger Bodenhilfsstoffe auf der Basis regional in der Lausitz verfügbarer Ressourcen untersucht.
Durch die Tagebauaktivitäten in der Region fallen große Mengen an Eisenhydroxidschlämmen (EHS) an. Hierbei lassen sich 3 Herkunftsschwerpunkte definieren: I) natürliche Eisenhydroxidsedimente aus Fließen, II) EHS aus Grubenwasserbehandlungsanlagen, die durch Kalkung gefällt wurden und III) EHS aus Trinkwassergewinnungsstätten, die gekalkt und zusätzlich durch chemische Flockungsmittel behandelt wurden. Diese sollen mit verschiedenen Tonen und Lehmen als weitere anorganische Komponenten, sowie mit organischem Material (Kompost) gemischt werden. Ziel ist die Herstellung von anorganisch-organische Mischsubstraten, die die bodenphysikalischen Eigenschaften positiv beeinflussen.
Erste Untersuchungsergebnisse zeigen, dass sich die untersuchten EHS je nach Behandlung in ihrer mineralogischen Zusammensetzung, sowie den physikochemischen Parametern deutlich unterscheiden. In ersten Experimenten wird untersucht, wie sich diese Mischungen auf die Wasserhaltekapazität von sandigen Böden auswirken und ob sich dabei Unterschiede zwischen den EHS und in Kombination mit den anderen Ausgangssubstanzen aufzeigen lassen.
Editorial for the Special Issue “Anthropogenic Geomorphology – Geomorphology in the Anthropocene”
(2023)
Tree and shrub species grown on post-mining sites as source of biomass for bio-based industry
(2021)
Reclamation sites in East-Germany are characterized by very sandy substrates being almost free of organic matter, with extremely low nutrient contents and an annual precipitation of only 570 mm and lower. To enhance the value of such marginal lands for a future regionally based bioeconomy the EU funded BeonNAT project plans the plantation of regional tree and shrub species. The obtained forest biomass will be used for the production of biodegradable bio-based products and bioactive compounds that will play an important role to replace fossil-based competing substitute products. Field trials in different European countries will be established to test the benefit of intercropping/mixed-forest in marginal cultural land versus the natural growing/monoculture. In Germany, one test site is located in the post-mining sites of Lusatia. In this field trial the biomass development, the impact on soil fertility and biodiversity will be investigated after planting Robinia pseudoacacia and Rubus fructicosus.
Open-cast coal-mining creates a severe disturbance of ecological systems, resulting in post-mining areas that have lost their soils entirely and are exposed to extreme conditions. Nonetheless, these areas allow us to investigate the process of primary succession. We studied the first 12 years of soil tardigrade succession at ‘Chicken Creek’, an experimental site within the open-cast coal-mining area in Welzow Süd, Germany. A total of ten tardigrade genera and 13 species or species groups were identified at the site. Over time, the organic carbon content, proportion of silt and nutrient availability in the soil increased, and the soil particle size distribution and pH value changed significantly. The vegetation cover and nematode abundance increased, whereas tardigrade densities peaked in 2008 and decreased thereafter, as did their mean body size. Species replacement was obvious within this 12 year study period, and tardigrade species diversity increased, indicating that ecological niche diversity also increased throughout this time. Vegetation cover, soil particle size distribution and time were the most important factors influencing the tardigrade community. We also discuss further potential factors and the ability of terrestrial tardigrades to persist despite many environmental constraints, such as fluctuations in pH and moisture, and to colonize new habitats faster than most other animal groups.
In recent years, probabilistic solution to the inversion of electromagnetic induction (EMI) data has been progressively developed for non-invasive subsurface characterization. However, Bayesian inversion of EMI data using forward solvers based on full solution ofMaxwell's equation is associated with computationally expensive modelings, particularly for large-scale surveys. Here, we incorporated artificial neural network (ANN) with Bayesian inference to obtain subsurface electromagnetic conductivity image (EMCI) fromEMI data down to 10mdepth. In this respect, a complex EMI forward model was replaced by a trained neural network (ANN proxy forward function) that can be evaluated comparably rapidly. The accuracy of the ANN-based forward solver was examined using different synthetic subsurface models. The proposed methodology was applied on EMI data measured with a DUALEM-421 s sensor from 10 ha study site in the Alken Enge area of Denmark. We compared the inversely estimated EMCI with the counterpart obtained from a quasi-three-dimensional (quasi-3D) spatiallyconstrained deterministic algorithm as a standard code. The network training procedure was performed within fewminutes, and once itwas trained, the ANN-based forward solver returned roughly 150,000 model responses per second. This value for the EMI forward solverwas around 400, demonstrating the computational efficiency of the ANN proxy forward function. The theoretical simulations demonstrated that the ANN-based forward solver accurately mimics the EMI response within the training range. Moreover, the proposed inversion strategy successfully delineated the subsurface EMCI from Alken Enge area. This approach thus facilitates rapid and accurate subsurface conductivity imaging using Bayesian inversion of multi-configuration EMI data, which is particularly pertinent for large-scale measurements.
Conventional geophysical inversion techniques suffer from several limitations including computational cost, nonlinearity, non-uniqueness and dimensionality of the inverse problem. Successful inversion of geophysical data has been a major challenge for decades. Here, a novel approach based on deep learning (DL) inversion via convolutional neural network (CNN) is proposed to instantaneously estimate subsurface electrical conductivity (σ) layering from electromagnetic induction (EMI) data. In this respect, a fully convolutional network was trained on a large synthetic data set generated based on 1-D EMI forward model. The accuracy of the proposed approach was examined using several synthetic scenarios. Moreover, the trained network was used to find subsurface electromagnetic conductivity images (EMCIs) from EMI data measured along two transects from Chicken Creek catchment (Brandenburg, Germany). Dipole–dipole electrical resistivity tomography data were measured as well to obtain reference subsurface σ distributions down to a 6 m depth. The inversely estimated models were juxtaposed and compared with their counterparts obtained from a spatially constrained deterministic algorithm as a standard code. Theoretical simulations demonstrated a well performance of the algorithm even in the presence of noise in data. Moreover, application of the DL inversion for subsurface imaging from Chicken Creek catchment manifested the accuracy and robustness of the proposed approach for EMI inversion. This approach returns subsurface σ distribution directly from EMI data in a single step without any iterations. The proposed strategy simplifies considerably EMI inversion and allows for rapid and accurate estimation of subsurface EMCI from multiconfiguration EMI data.
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.
The cultivation of bioenergy plants in fertile, arable lands increasingly results in new land use conflicts with food production and cannot be considered as sustainable. Marginal lands have been frequently considered as potential alternatives for producing bioenergy from biomass. However, clear definitions and assessment methods for selecting marginal lands and for calculating potentials are still widely missing. The project “SEEMLA” aims at triggering the exploitation of currently underused marginal lands for biomass production for energy purposes. Study sites have been selected in different European countries: Germany, Greece, and Ukraine. The selected sites represent a wide variety of different types of marginal lands. Based on a soil assessment set given by the Muencheberg Soil Quality Rating (SQR) system potentially “marginal” sites have been investigated. The SQR system allows for clearly distinguishing between soils of higher and lower quality. Soils with SQR scores below 40 are regarded as “marginal”. They can be classified into different groups with regard to the importance of soil hazard indicators as evaluated by the SQR approach. The calculated SQR scores correlate significantly with biomass yields of bioenergy plants.
Further, the SQR method was adapted for use in a GIS study on marginal-land potentials in Europe. Thus, 46% of the investigated European area could be classified as “marginal” with SQR scores below 40. From that area 22.6% can be considered as potentially suitable for producing renewable resources after eliminating protected sites or other places not suitable for any kind of land use. Taking the ecological demands of selected bioenergy plants into account it is possible to give first preliminary recommendations for regional crop cultivation. It can be concluded that Europe offers a large potential for renewable resources from marginal sites. However, the implementation into practice is often impeded by missing or varying policies and regulations. A proper implementation needs clear regulations and also incentives for farmers at the European level.
Edge detection is one of the most important steps in the map interpretation of potential field data. In such a dataset, it is difficult to distinguish adjacent anomalous sources due to their field superposition. In particular, the presence of overlain shallow and
deep magnetic/gravity sources leads to strong and weak anomalies. In this paper, we present an improved filter, STDR, which utilises the ratio of the second-order vertical derivative to the second-order total horizontal derivative at the tilt angle equation. The maximum
and minimum values of this filter delineate the positive and negative anomalies, respectively. This novel filtering approach normalises the intensity of strong and weak anomalies, as well as anomalies with different depths and properties. Moreover, to better
illustrate the edges, its total horizontal derivative (THD_STDR) is also used. For positive and negative anomalies, the maximum value of the THD_STDR filter shows the edges of the anomalies. The potentiality of the proposed method is examined through both
synthetic and real case scenarios and the results are compared with a number of existing edge detector filters, namely TDR, THD_TDR, Theta and TDX. Due to substantial improvements in the filtering, STDR and its total horizontal derivative allow for
more accurate estimation of anomaly edges in comparison with the other filtering techniques. As a consequence, the interpretation of the potential field data is more feasible using the STDR filtering method.
Potential field methods produce anomaly maps with different magnitudes and depths that are typically contaminated by noise, making them hard to interpret. In order to highlight edges of the anomalies with different depths and magnitudes, data filtering techniques have received a great attention, in particular for mineral explorations. Filtering approaches render to explore more details from potential field data maps. In this respect, high pass filters are commonly used for enhancing the anomaly edges all of which utilize gradients of the potential field. In order to apply different filters on the potential field data, major attempts have been made to make a balance between noise and the signal obtained from a filtered image (Cooper & Cowan, 2006).
High-Resolution Soil Electrical Conductivity Imaging from EMI D Based Probabilistic Inversion
(2018)
Electromagnetic induction (EMI) sensors allow for non-invasive soil characterizations. Proximal soil sensing using EMI hindered due to the problems related to the inversion of apparent electrical conductivity (ECa) data. In this study, I used Bayesian inference to obtain the electrical conductivity layering of the subsurface from multi-configuration EMI data. In this respect, generalized formal likelihood function was used to more accurately describe the sensitivity of the posterior parameter 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. I considered apparent electrical conductivity pseudosection as a training image (TI) in multiple-point statistical simulations. Information from TI realizations were utilized to determine dominant DCT coefficients, as well as prior probability density functions for the subsequent probabilistic inversions. The potentiality of the proposed approach was examined through an experimental scenario. The results demonstrated that this methodology allows for soil electrical conductivity imaging with high resolution. This strategy permits to incorporate summary metrics from ensemble of ECa pseudosection realizations in the inversion without resorting to any complimentary source of information. The proposed approach ensures accurate and high resolution characterization of soil conductivity layering from measured ECa values.
Landscapes and ecosystems are complex systems with many feedback mechanisms acting between the various abiotic and biotic components. The knowledge about these interacting processes is mainly derived from mature ecosystems. The initial development of ecosystem complexity may involve state transitions following catastrophic shifts, disturbances, or transgression of thresholds. We propose a conceptual framework of feedback processes in early states of ecosystem development affected by spatiotemporal environmental drivers. To test this concept, we used 10-year time series of hydrological, biological, geomorphological, and soil data from the constructed catchment Chicken Creek.” The 6ha site was left to unrestricted development since 2005 and was intensively monitored. The data showed a very rapid development of the site with an increasing complexity and heterogeneity. In the first years, stochastic signals like the initial substrate conditions and external drivers like extreme weather events were the most important factors resulting in abiotic/abiotic feedback mechanisms shaping the morphology of the site and creating site diversity. Initial abiotic feedback mechanisms between water and substrate were soon followed by abiotic/biotic feedbacks between biological soil crusts, invading vegetation, geomorphology, and hydrology resulting in state transitions of catchment functioning.
To simulate the impacts of within-storm rainfall variabilities on fast hydrological processes, long precipi-tation time series with high temporal resolution are required. Due to limited availability of observed data such time series are typically obtained from stochastic models. However, most existing rainfall models are limited in their ability to conserve rainfall event statistics which are relevant for hydrological pro-cesses. Poisson rectangular pulse models are widely applied to generate long time series of alternating precipitation events durations and mean intensities as well as interstorm period durations. Multiplicative microcanonical random cascade (MRC) models are used to disaggregate precipitation time series from coarse to fine temporal resolution.
To overcome the inconsistencies between the temporal structure of the Poisson rectangular pulse model and the MRC model, we developed a new coupling approach by introducing two modifications to the MRC model. These modifications comprise (a) a modified cascade model (“constrained cascade”) which preserves the event durations generated by the Poisson rectangular model by constraining the first and last interval of a precipitation event to contain precipitation and (b) continuous sigmoid func-tions of the multiplicative weights to consider the scale-dependency in the disaggregation of precipita-tion events of different durations. The constrained cascade model was evaluated in its ability to dis-aggregate observed precipitation events in comparison to existing MRC models. For that, we used a 20-year record of hourly precipitation at six stations across Germany. The constrained cascade model showed a pronounced better agreement with the observed data in terms of both the temporal pattern of the precipitation time series (e.g. the dry and wet spell durations and autocorrelations) and event char-acteristics (e.g. intra-event intermittency and intensity fluctuation within events). The constrained cas-cade model also slightly outperformed the other MRC models with respect to the intensity-frequency relationship.
To assess the performance of the coupled Poisson rectangular pulse and constrained cascade model, precipitation events were stochastically generated by the Poisson rectangular pulse model and then disaggregated by the constrained cascade model. We found that the coupled model performs satisfac-torily in terms of the temporal pattern of the precipitation time series, event characteristics and the in-tensity-frequency relationship.
Marginal land (MagL) use for biomass production has received a lot of attention in the past decades. This alternative land use is free from conflicts with food crops, however it presents multiple challenges. The major issue to be resolved is the definition of marginal lands, which varies between disciplines and is often determined based on management goals. Moreover, concerns over the impacts of MagL use on environment, ecosystem services and sustainability have to be addressed.
In response to these issues, the SEEMLA approach proposes an algorithm and tools to support MagLs identification and mapping, as well as the assessment of their potential for biomass production. The SEEMLA algorithm assesses land marginality using the Muencheberg Soil Quality Rating (M-SQR) developed by Mueller et al. (2007). The algorithm also incorporates criteria to eliminate MagLs that may not be exploitable for bioenergy due to ecological and environmental issues or regulatory and legal restrictions and constraints posed by national or EU policies applied. Finally, MagL suitable for bioenergy production is categorized based on climatic zone and crop suitability. Alternative exploitation scenarios are developed for each class of MagL. Life Cycle Assessment (LCA) will determine the sustainability of each scenario, considering also the results of the pilot sites that have been established in Germany, Greece and Ukraine.
The cultivation of bioenergy plants at fertile, arable lands increasingly results in new land use conflicts with food production and cannot be considered as sustainable, therefore. Marginal lands have been frequently considered as potential alternative for producing bioenergy from biomass. However, clear definitions and assessment methods for selecting marginal lands and for calculating potentials are still widely missing. The project “SEEMLA” aims at triggering the exploitation of currently underused marginal lands for biomass production for energy purposes. Study sites have been selected in different European countries: Germany, Greece and Ukraine. The selected sites represent a wide variety of different types of marginal lands. Based on a soil assessment set given by the Muencheberg Soil Quality Rating (SQR) system potentially “marginal” sites have been investigated. The SQR system allows for clearly distinguishing between soils of higher and lower quality. Soils with SQR scores below 40 are regarded as “marginal”. They can be classified into different groups with regard to the importance of soil hazard indicators as valuated by the SQR approach. The calculated SQR scores correlate significantly with biomass yields of bioenergy plants. Further, the SQR method was adapted for use in a GIS study on marginal land potentials in Europe. 46 % of the investigated European area could be classified as “marginal” with SQR scores below 40. From that area 22.6 % can be considered as potentially suitable for producing renewable resources after eliminating protected sites or other places not suitable for any kind of land use. Taking the ecological demands of selected bioenergy plants into account it is possible to give first preliminary recommendations for regional crop cultivation. It can be concluded, that Europe offers a large potential for renewable resources from marginal sites. However, the implementation into practice is often impeded by missing or varying policies and regulation. A proper implementation needs clear regulations and also incentives for farmers at European level.
Das Hühnerwasser-Einzugsgebiet ist ein 6 ha großes, künstlich geschaffenes Wassereinzugsgebiet im Tagebau Welzow-Süd in Südost-Brandenburg. Das Zweischichtsystem mit einer Ton- und einer darüber liegenden Sandschicht wurde im Jahr 2005 fertiggestellt und ohne weitere Rekultivierungsmaßnahmen für Forschungszwecke bereitgestellt. Es lässt exemplarisch die Untersuchung der initialen Landschaftsgenese zu. Insbesondere die Entwicklung von einem zunächst noch abiotisch kontrollierten hin zu einem biotisch gesteuerten System lässt sich hier beobachten. In diesem Beitrag werden beispielhaft einige in diesem Kontext aufgetretene Interaktionen zwischen abiotischen und biotischen Strukturelementen vorgestellt. Dies sind zum einen Erosions- und Sedimentationsprozesse und ihre Wechselwirkungen mit der Vegetation. Zum anderen wird der Einfluss der Vegetation auf die Grundwasserentwicklung dargestellt.
Low frequency loop-loop electromagnetic induction (EMI) is widely used for monitoring soil electrical conductivity and water content. As a non-invasive geophysical technique, EMI allows for rapid and real-time electrical conductivity measurements. However, EMI has not yet been used much to back out the vertical (depth profile) conductivity structure due to problems with the inversion of measured apparent electrical conductivity (ECa) data. In this study, we used Bayesian inference with the MT-DREAM(ZS) algorithm to infer the electrical conductivity layering of the subsurface from EMI data.We test and evaluate our methodology using apparent electrical conductivity data measured along two transects in the Hühnerwasser catchment in Lusatia, Germany. These measurements were made using CMD-Explorer, a multi-configuration sensor with three inter-coil spacings and two antenna orientations. Three offsets and two antenna modes lead to six measurement depths. Electrical Resistivity Tomography (ERT) measurements were also carried out to provide reference conductivity values and to calibrate the EMI data. Such calibration is necessary for quantitative interpretation of the ECa values and to enable multi-layered inversion. The Discrete Cosine Transform (DCT) was used to reduce the number of unknown parameters, and different likelihood functions were used to evaluate the sensitivity of the posterior parameter distribution to residual assumptions. DCT-based inversion equates to a quasi-two-dimensional framework which incorporates all data along the profile and results in a low-dimensional over-determined optimization problem. Results demonstrated that although appropriate selection of the low frequency DCT coefficients is important, the definition of the likelihood function plays a crucial role in the estimation of parameter and predictive uncertainty. The use of a Gaussian likelihood function introduces artifacts in DCT-based inversion of EMI data. The use of a more flexible likelihood function results in more accurate results of the DCT-inversion. Integration of the DCT with the MT-DREAM(ZS) algorithm and a flexible generalized likelihood function appears promising for the inversion of low frequency loop-loop EMI data. The proposed approach promises accurate and high resolution estimation of subsurface hydrogeophysical properties from EMI data.
Experimental catchments with well-known boundaries and characteristics may contribute valuable data to hydrological, critical zone and landscape evolution research. One of the most well-established and largest constructed catchments is the Chicken Creek catchment (6 ha area including a 0.4 ha pond, Brandenburg, Germany) representing an initial ecosystem undergoing a highly dynamic ecological development starting from clearly defined starting conditions. The water balance dynamics of the catchment was calculated using a simple mass balance approach to reveal the impact of ecological development during 12 years. Water storage in the catchment was calculated from a 3D-model of groundwater volumes, soil moisture measurements and water level recordings of the pond. The catchment water balance equation was resolved for evapotranspiration, the only part that was not measured directly. Due to the known boundary conditions and the inner structure of the catchment, we were able to quantify the different storage compartments and their role in hydrologic response. Our results indicate that for small catchments with a highly dynamic ecological development like the Chicken Creek, the knowledge about saturated and unsaturated storage volumes enables a good estimate and closure of the water balance using a rather simple approach, at least in annual resolution. We found a significant relationship between vegetation cover in the catchment and calculated ET. Time series of meteorological, hydrological, soil and vegetation data over 12 years enabled us to characterize the transient development of the catchment and to evaluate the effect of different feedback mechanisms on catchment hydrology. The dataset from the Chicken Creek catchment indicate at least three phases in ecosystem development, where initial abiotic feedbacks (e.g. erosion) were followed by more and
more biotic controls (e.g. biological soil crusts, vegetation succession and growth). Data from Chicken Creek in high spatial and temporal resolution provide a valuable database underlining the high importance of abiotic/biotic feedback effects that change the hydrologic functioning and response of the catchment more than the water balance itself revealed and thus have to be included in catchment models.
The spatial variability of soil physical properties on the landscape scale is often increased by anthropogenic land occupation, not only by current land use but also through the legacies of past land use systems. The remains of historic charcoal hearts are an example for such a disturbance of the soil landscape by former forestry. Such relict charcoal hearths (RCH) exhibit a clearly altered soil stratigraphy, most prominently characterized by a technogenic substrate layer on the soil surface, and their soil physical properties can considerably differ from those of surrounding forest soils. The aim of our study is to characterize the soil water and temperature regime on RHC in a pre-industrial charcoal production area in Brandenburg, Germany, as compared with the surrounding sandy forest soils. Soil properties were analyzed in profiles on and around hearth sites and are monitored in a sensor transect equipped with soil
moisture sensors and pF-meters. Results of soil sample analyses show differences in density and porosity between the RCH soils and surrounding forest soils, not only in the technogenic layer but also in the buried soil layers on hearth sites. The soil water characteristic curves determined in the laboratory indicate a modified pore size distribution and lower plant available water contents in the RCH soils. Preliminary results of the ongoing soil water monitoring, however, show increased soil wetness in the RCH soils, along with lower soil moisture tensions. Furthermore, the measurements show higher variations of soil temperature in RCH soils. The results affirm that the legacies of historic charcoal production can increase the spatial variability of soil physical properties and therefore also of ecological site conditions in forest areas. The results of soil moisture monitoring suggest that a determination of soil physical parameters in the laboratory is not sufficient to characterize the spatio-temporal variations of the soil water regime.
Multidimensional visualization of preferential flow pathways using neutron and x-ray radiation
(2017)
Monitoring spatiotemporal variations of soil water content (θ) is important across a range of research fields, including agricultural engineering, hydrology, meteorology and climatology. Low frequency electromagnetic induction (EMI) systems have proven to be useful tools in mapping soil apparent electrical conductivity (σa) and soil moisture. However, obtaining depth profile water content is an area that has not been fully explored using EMI. To examine this, we performed time-lapse EMI measurements using a CMD mini-Explorer sensor along a 10m transect of a maize field over a 6 day period. Reference data were measured at the end of the profile via an excavated pit using 5TE capacitance sensors. In order to derive a time-lapse, depth-specific subsurface image of electrical conductivity (σ), we applied a probabilistic sampling approach, DREAM(ZS), on the measured EMI data. The inversely estimated σ values were subsequently converted to θ using the Rhoades et al. (1976) petrophysical relationship. The uncertainties in measured σa, as well as inaccuracies in the inverted data, introduced some discrepancies between estimated σ and reference values in time and space. Moreover, the disparity between the measurement footprints of the 5TE and CMD Mini-Explorer sensors also led to differences. The obtained θ permitted an accurate monitoring of the spatiotemporal distribution and variation of soil water content due to root water uptake and evaporation. The proposed EMI measurement and modeling technique also allowed for detecting temporal root zone soil moisture variations. The time-lapse θ monitoring approach developed using
DREAM(ZS) thus appears to be a useful technique to understand spatiotemporal patterns of soil water content and provide insights into linked soil moisture vegetation processes and the dynamics of soil moisture/infiltration processes.
Difficulties in quantitatively closing the water balance of catchments arise when upscaling point measurements and from insufficient knowledge of the physical boundaries, inner structure, and storage volumes of natural catchments. In addition, there is a strong need for generalizing the relationship between catchment characteristics and hydrological response. Therefore, experimental catchments with well-known boundaries and conditions could contribute valuable data to hydrological and critical zone research. One of the most well-established and largest constructed catchments is the Chicken Creek catchment (6 ha including a pond, Brandenburg, Germany) representing an initial ecosystem undergoing highly dynamic ecological development starting from clearly defined starting conditions. Directly after completion of the construction, extensive monitoring equipment was installed to track the ecosystem development and to capture the
spatiotemporal variability of meteorological, hydrological, ecological, and soil conditions and vegetation succession. In this study, we focused on the water balance dynamics of the Chicken Creek catchment for the period 2005 to 2015 as influenced by ecological development. Water storage in the catchment was calculated from a three-dimensional model of groundwater volumes, soil moisture measurements, and water level recordings of the pond. The catchment water balance equation was resolved for evapotranspiration, the only part that was not measured directly. Time series of meteorological, hydrological, and ecological data for 10 yr enabled us to characterize the transient development of the catchment and to evaluate the effect of different feedback mechanisms on catchment hydrology.
A substantial interpretation of electromagnetic induction (EMI) measurements requires quantifying optimal model parameters and uncertainty of a nonlinear inverse problem. For this purpose, an adaptive Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to assess multi-orientation and multi-offset EMI measurements in an agriculture field with non-saline and saline soil. In MCMC the posterior distribution is computed using Bayes’ rule. The electromagnetic forward model based on the full solution of Maxwell’s equations was used to simulate the apparent electrical conductivity measured with the configurations of EMI instrument, the CMD Mini-Explorer. Uncertainty in the parameters for the three-layered earth model are investigated by using synthetic data. Our results show that in the scenario of non-saline soil, the parameters of layer thickness as compared to layers electrical conductivity are not very informative and are therefore difficult to resolve. Application of the proposed MCMC-based inversion to field measurements in a drip irrigation system demonstrates that the parameters of the model can be well estimated for the saline soil as compared
to the non-saline soil, and provides useful insight about parameter uncertainty for the assessment of the model outputs.
Historical charcoal production has long been an unconsidered type of forest utilization in lowland areas. The archaeological discovery of a large charcoal burning area in the forefield of the active opencast lignite mine J€anschwalde in SE Brandenburg (North German Lowlands) has highlighted the need for multidisciplinary research in the study of the dimensions of historical charcoal burning. The charcoal produced in our study area, the former royal forest district Tauer, provided energy for the nearby Peitz ironworks during its operation from the mid-16th to the mid-19th century. Here, we aimed to record the amount, size and distribution of relict charcoal hearths (RCHs) to examine their context with respect to the ironworks and to better understand the potential impacts on late Quaternary landscape change. The applied methods combined archaeological research with shaded-relief mapping of RCHs. To evaluate the detection success of RCHs in the shaded-relief maps (SRMs), we compared the results with the results from specific archaeological surveys and conventional forest site mapping. Furthermore, we analyzed our dataset with descriptive, comparable and spatial statistics. Additionally, dendrochronological dating of charcoal fragments from selected RCHs was used to investigate the temporal relationships among the RCHs. Approximately 6000 circular RCHs were manually digitally identified in the SRMs of the 109-km2 evaluable area. However, ground truthing showed that the shaded-relief maps could not completely capture all RCHs in our study area due to local conditions. At the regional scale, two focus areas with high RCH site densities were found. Statistical analysis revealed that the diameters of the RCHs range widely from 4 to 30.5 m. Spatial statistics were applied to the RCH diameters to aggregate areas with larger or smaller RCHs. At the small scale, no systematic arrangement of RCHs was obvious in the SRMs. Dendrochronological dating of selected RCHs lying close together could not detect significant spatiotemporal relationships or patterns. The dating results also showed that some areas have been preferentially used for charcoal production for over 300 years. Due to the homogeneity of the physiogeographical location factors, we assume that these factors are not superficially responsible for site selection. Instead, economic location factors and contemporary regulations are more likely responsible for the distribution of RCHs.
Eine zunehmende Knappheit ackerbaulicher Standorte und der zugleich steigende Bedarf an nachwachsenden Rohstoffen für die energetische und stoffliche Nutzung lösen verstärkt Konflikte zwischen der Nahrungsmittelproduktion und der Biomasseproduktion aus. Marginale Standorte kommen entsprechend immer stärker in den Blickpunkt als mögliche Erweiterungsflächen für die Biomasseproduktion. In der wissenschaftlichen Auseinandersetzung fehlt allerdings eine eindeutige Definition für derartige marginale Standorte. Gerade im Hinblick auf eine mögliche Reduzierung von Nutzungskonflikten ist die Abgrenzung zu stillgelegten, aber agrarisch nutzbaren Standorten oder zu natürlichen Badlands, mit naturschutzfachlichen Potentialen oftmals schwierig. Das EU H2020-Projekt SEEMLA untersucht Potentiale und Verfügbarkeit von marginalen Standorten europaweit. In Deutschland, Griechenland und der Ukraine wurden Pilotstandorte eingerichtet, auf denen der Anbau von Bioenergiepflanzen exemplarisch durchgeführt wird. Für eine erste Bewertung der Standortspotentiale und auch der Ursachen der Marginalität der Standorte wurden Bodenuntersuchungen an allen Pilotstandorten nach den Vorgaben des Muencheberger Soil Quality Rating-Verfahrens (SQR) (1) durchgeführt und die Standorte klassifiziert. Das SQR-Verfahren bewertet ausgewählte Boden- und Standortseigenschaften und bezieht zusätzlich mögliche limitierende Faktoren (Hazard Indicators) wie Versauerung, Versalzung, Vernässung etc. mit ein, um eine Gesamtbewertungszahl zur Bodenfruchtbarkeit zu ermitteln. Dieser Beitrag stellt anhand der SEEMLA-Standorte exemplarisch Böden marginaler Standorte vor. Mit Hilfe der SQR Hazard Indicators werden charakteristische limitierende Faktoren vorgestellt. Es wird zudem versucht, die untersuchten Standorte in eine mögliche Klassifikation marginaler Standorte einzupassen und ihre Eignung für die Biomasseproduktion einzuschätzen.
An artificial catchment was constructed to study initial soil and ecosystem development. Our research site is the hydrologic catchment area “Chicken Creek”, 20 km south of the city Cottbus (Germany), which is left to undirected succession. The approximately 6-ha catchment area was built in 2005 of coarse-textured quaternary sediments and forms a back- and foot-slope that flattens out to a pond. A clay liner, 2-3 m in thickness, seals the catchment at the bottom. The initial “soil” layer consists of sandy quaternary sediments, approximately 2–3 m in thickness, that overlay the clay liner. The sediments of the artificial system are in the very initial stage of soil formation. As a key process, the pore structure dynamics in the soil at the surface strongly influences erosion, infiltration, matter dynamics, and vegetation establishment. Little is known, however, about early macropore formation. This presentation focuses on observations of the general development of the catchment, with emphasis on soil structure modifications, such as soil surface crusts, structure formation from ground beetles, moss vegetation and its effect on water flow.
We visualized the soil structure and flow patterns using a combination of neutron radiography, neutron tomography (NT) and X-ray computed tomography (CT) experiments. All techniques were used to describe preferential pathways as a soil
structure with CT and the process of preferential flow of water with NT. For the first time, neutron radiation was applied on undisturbed soil cores to visualize water distributions in natural soils on three examples containing heterogeneities of different origin. Our observations demonstrate relatively high abiotic and biotic dynamics of soil pore structure in the soil surface even during the very early development stages of the catchment. The structure formation influences runoff and infiltration by forming sealing layers or preferential flow paths, due to biotic activity.
The term “marginal land” refers to generally unfavourable site conditions for traditional economic agriculture or forestry. The definitions of marginality differ widely and also the scientific perspective on this type of land is very heterogeneous. However, with regard to the fast growing competition for arable lands between agricultural food production and the increasing need for growing renewable resources, such marginal lands could offer a mitigation option. Whereas agriculture is concentrating rapidly on the most fertile soils, the production of biomass as the renewable basis for various products could be shifted to sites with lower soil fertility. This in general is the main objective of the H2020 funded EU project SEEMLA (acronym for “Sustainable exploitation of biomass for bioenergy from marginal lands”). This project aims at an establishment of suitable innovative land-use strategies for a sustainable production of plant-based energy on marginal lands, while improving general ecosystem services. This approach will be tested in selected casestudy regions in Europe on marginal lands with different reasons for their marginality. One of the central tasks of this project was the characterization and classification of the investigated marginal sites according to a common definition. For that reason the Muencheberg Soil Quality Rating (SQR) system1 was adapted and applied at sites
in Germany, Greece and Ukraine. This paper gives an overview of the importance and properties of different soil parameters and ecological site conditions for this classification. Even if the occurrence of the single site limitations epends on regional conditions, some generalizations are possible. Based on the respective dominating soil related restrictions, a classification of types of marginality and of marginal lands can be derived also with regard to their land use potentials for renewable resources. Further, cultivating biomass on marginal lands may offer remediation potentials with regard to the described ecological restraints of marginal sites.
Landscapes and ecosystems are complex systems with many feedback mechanisms acting between the various abiotic and biotic components. The knowledge about these interacting processes is mainly derived from mature ecosystems. The initial development of ecosystem complexity may involve state transitions following catastrophic shifts, disturbances or transgression of thresholds. The Chicken Creek catchment was constructed in 2005 to study ecosystem development
of an initial ecosystem at the landscape scale. The world´s largest constructed catchment has a hillslope-shaped 6 ha size with defined boundary conditions and well-documented inner structures1. For 12 years, we have been monitoring the development of different system compartments2,3. The fast formation of patterns and increasing heterogeneity were challenges for the monitoring program. Starting with a regular 20 × 20 m grid in the initially homogeneous system, monitoring installations were continuously complemented by more pattern and patch oriented measurements in order to catch up with both the spatial and temporal dynamics of the catchment. The monitoring program includes both
high-resolution temporal recordings (e.g., groundwater levels, discharge, meteorological data, soil moisture), spatial sampling campaigns (e.g., soil properties) and annual vegetation surveys. In addition, we use drone images to document the site development. The presentation describes the monitoring approach and adaptation with examples from vegetation, soil and hydrological data at different spatial and temporal scales. From the monitoring data we were able to derive different phases during initial development that are characterized by abiotic/biotic feedback mechanisms controlling catchment functioning. Detailed monitoring of the Chicken Creek catchment with known boundary conditions and structure information could help in disentangling general feedback mechanisms between hydrologic, pedogenic, biological and geomorphological processes as well as in gaining a more integrative view of succession and its drivers during the transition from initial, less complex systems to more mature ecosystems. Long-term time series of data are the key for a better understanding of these processes and the effects on ecosystem self-organization and resilience.