Forschungszentrum Landschaftsentwicklung und Bergbaulandschaften (FZLB)
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- deep soil loosening (1)
- lignite mine (1)
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BTU
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.
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.
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.
The Chicken Creek catchment was constructed in 2005 to study ecosystem development of an initial ecosystem at the landscape scale. The site has an area of 6 ha with defined boundary conditions and known inner structures. Since more than 10 years, we are monitoring the unrestricted development of different system compartments. The fast development, the formation of patterns and increasing heterogeneity were challenges for the monitoring program. Starting with a regular 20 x 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.
To improve the quality of parameter optimization, estimability analysis has been proposed as the first step before inverse modeling. When using field data of irrigation experiments for the determination of soil hydraulic parameters, wetting and drying processes may complicate optimization. The objectives of this study were to compare estimability analysis and inverse optimization of the soil hydraulic parameters in the models with and without considering hysteresis of the soil water retention function. Soil water pressure head data of a field irrigation experiment were used. The one-dimensional vertical water movement in variably saturated soil was described with the Richards equation using the HYDRUS-1D code. Estimability of the unimodal van Genuchten–Mualem hydraulic model parameters as well as of the hysteretic parameter model of Parker and Lenhard was classified according to a sensitivity coefficient matrix. The matrix was obtained by sequentially calculating effects of initial parameter variations on changes in the simulated pressure head values.Optimization was carried out by means of the Levenberg-Marquardt method implemented in the HYDRUS-1D code. The parameters α, Ks, θs, and n in the nonhysteretic model were found sensitive and parameter θs strongly correlated with parameter n. When assuming hysteresis, the estimability was decreased with soil depth for Ks and αd , and increased for θs and n. Among the shape parameters, αw was the most estimable. The hysteretic model could approximate the pressure heads in the soil by considering parameters from wetting and drying periods separately as initial estimates. The inverse optimization could be carried out more efficiently with most estimable parameters. Despite the remaining weaknesses of the local optimization algorithm and the inflexibility of the unimodal van Genuchten model, the results suggested that estimability analysis could be considered as a guidance to better define the optimization scenarios and then improved the determination of soil hydraulic parameters.
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.
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.
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.