@misc{MoghadasTaghizadehMehrjardiTriantafilis, author = {Moghadas, Davood and Taghizadeh-Mehrjardi, Ruhollah and Triantafilis, John}, title = {Probabilistic inversion of EM38 data for 3D soil mapping in central Iran}, series = {Geoderma Regional}, volume = {7}, journal = {Geoderma Regional}, number = {2}, doi = {10.1016/j.geodrs.2016.04.006}, pages = {230 -- 238}, abstract = {Accurate determination of near surface soil electrical properties is important for agricultural and environmental management. In this respect, low frequency electromagnetic induction (EMI) has been widely used to measure soil apparent electrical conductivity. However, the potential to model soil subsurface layering has not been fully realized using EMI data. In this paper, we applied a probabilistic optimization approach, namely DREAM (ZS), on Geonics EM38 data to explore the robustness of this approach for soil subsurface conductivity mapping. The EM38 data was measured from the Ardakan region in the province of Yazd located in central Iran. Several soil sampleswere taken andwere further analyzed in a laboratory to derive soil textural and physical parameters. The probabilistic inversion was performed in a joint multi-configuration framework considering a five layered model. The estimated values are mainly in agreementwith the clay map (as the most influential factor); nevertheless, soil salinity data and inversely estimated conductivity values are poorly correlated for deeper layers due to the aridic condition and high clay content in the study area. DREAM (ZS) optimization approach appears to be promising for accurate retrieval of soil conductivity depth profile from EM38 data.}, language = {en} } @misc{JadoonMcCabeAltafetal., author = {Jadoon, Khan Zaib and McCabe, Matthew F. and Altaf, Muhammad Umer and Hoteit, Ibrahim and Muhammad, Nisar and Moghadas, Davood and Weiherm{\"u}ller, Lutz}, title = {Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo}, series = {Hydrology and Earth System Sciences}, volume = {21}, journal = {Hydrology and Earth System Sciences}, number = {10}, issn = {1607-7938}, doi = {10.5194/hess-21-5375-2017}, pages = {5375 -- 5383}, abstract = {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.}, language = {en} } @misc{MoghadasJadoonMcCabe, author = {Moghadas, Davood and Jadoon, Khan Zaib and McCabe, Matthew F.}, title = {Spatiotemporal monitoring of soil water content profiles in an irrigated field using probabilistic inversion of time-lapse EMI data}, series = {Advances in Water Resources}, volume = {110}, journal = {Advances in Water Resources}, issn = {1872-9657}, doi = {10.1016/j.advwatres.2017.10.019}, pages = {238 -- 248}, abstract = {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.}, language = {en} } @inproceedings{MoghadasVrugt, author = {Moghadas, Davood and Vrugt, Jasper A.}, title = {Non-invasive characterization of soil conductivity structure using probabilistic inversion and dimensionality reduction approach}, series = {European Geosciences Union, General Assembly 2018, Vienna, Austria}, booktitle = {European Geosciences Union, General Assembly 2018, Vienna, Austria}, publisher = {European Geophysical Society}, address = {Katlenburg-Lindau}, abstract = {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 H{\"u}hnerwasser 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.}, language = {en} } @misc{NasutiNasutiMoghadas, author = {Nasuti, Yasin and Nasuti, Aziz and Moghadas, Davood}, title = {STDR: A Novel Approach for Enhancing and Edge Detection of Potential Field Data}, series = {Pure and Applied Geophysics}, journal = {Pure and Applied Geophysics}, number = {24}, issn = {0033-4553}, doi = {10.1007/s00024-018-2016-5}, pages = {15}, abstract = {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.}, language = {en} } @misc{MoghadasVrugt, author = {Moghadas, Davood and Vrugt, Jasper A.}, title = {The influence of geostatistical prior modeling on the solution of DCT-based Bayesian inversion: A case study from Chicken Creek catchment}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {13}, issn = {2072-4292}, doi = {10.3390/rs11131549}, pages = {22}, abstract = {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.}, language = {en} } @misc{MoghadasJadoonMcCabe, author = {Moghadas, Davood and Jadoon, Khan Zaib and McCabe, Matthew F.}, title = {Spatiotemporal monitoring of soil moisture from EMI data using DCT-based Bayesian inference and neural network}, series = {Journal of Applied Geophysics}, volume = {169}, journal = {Journal of Applied Geophysics}, issn = {0926-9851}, doi = {10.1016/j.jappgeo.2019.07.004}, pages = {226 -- 238}, abstract = {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.}, language = {en} } @misc{MoghadasBadorreck, author = {Moghadas, Davood and Badorreck, Annika}, title = {Characterization of soil electrical conductivity from Chicken Creek Catchment using deep learning inversion of geophysical data}, series = {EGU General Assembly 2020, Online, 4-8 May 2020}, journal = {EGU General Assembly 2020, Online, 4-8 May 2020}, doi = {10.5194/egusphere-egu2020-2664}, language = {en} } @inproceedings{NasutiNasutiMoghadas, author = {Nasuti, Aziz and Nasuti, Yasin and Moghadas, Davood}, title = {Enhancing Potential Field Data Using TDY Filter}, series = {Near Surface Geoscience Conference \& Exhibition, 9-12 September 2018, Porto, Portugal}, booktitle = {Near Surface Geoscience Conference \& Exhibition, 9-12 September 2018, Porto, Portugal}, abstract = {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).}, language = {en} } @inproceedings{Moghadas, author = {Moghadas, Davood}, title = {High-Resolution Soil Electrical Conductivity Imaging from EMI D Based Probabilistic Inversion}, series = {24th European Meeting of Environmental and Engineering Geophysics, 9-13 September Porto, Portugal}, booktitle = {24th European Meeting of Environmental and Engineering Geophysics, 9-13 September Porto, Portugal}, abstract = {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.}, language = {en} } @misc{MoghadasBehroozmandChristiansen, author = {Moghadas, Davood and Behroozmand, Ahmad A. and Christiansen, Anders Vest}, title = {Soil electrical conductivity imaging using a neural network-based forward solver: Applied to large-scale Bayesian electromagnetic inversion}, series = {Journal of Applied Geophysics}, volume = {176}, journal = {Journal of Applied Geophysics}, issn = {0926-9851}, doi = {10.1016/j.jappgeo.2020.104012}, pages = {12}, abstract = {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.}, language = {en} } @misc{Moghadas, author = {Moghadas, Davood}, title = {One-dimensional deep learning inversion of electromagnetic induction data using convolutional neural network}, series = {Geophysical Journal International}, volume = {222}, journal = {Geophysical Journal International}, number = {1}, issn = {1365-246X}, doi = {10.1093/gji/ggaa161}, pages = {247 -- 259}, abstract = {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.}, language = {en} } @misc{MoghadasBadorreck, author = {Moghadas, Davood and Badorreck, Annika}, title = {Soil Moisture Patterns in an artificial water catchment - A machine learning approach from geophysical measurements}, series = {EGU General Assembly 2020, Online, 4-8 May 2020}, journal = {EGU General Assembly 2020, Online, 4-8 May 2020}, doi = {10.5194/egusphere-egu2020-2652}, language = {en} } @misc{MoghadasBadorreck, author = {Moghadas, Davood and Badorreck, Annika}, title = {Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity}, series = {Near Surface Geophysics}, volume = {17}, journal = {Near Surface Geophysics}, number = {2}, issn = {1569-4445}, doi = {10.1002/nsg.12036}, pages = {181 -- 195}, abstract = {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.}, language = {en} } @misc{Moghadas, author = {Moghadas, Davood}, title = {Probabilistic Inversion of Multiconfiguration Electromagnetic Induction Data Using Dimensionality Reduction Technique: A Numerical Study}, series = {Vadose Zone Journal}, volume = {18}, journal = {Vadose Zone Journal}, number = {1}, doi = {10.2136/vzj2018.09.0183}, pages = {16}, abstract = {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.}, language = {en} } @misc{MoghadasSchaafGerwinetal., author = {Moghadas, Davood and Schaaf, Wolfgang and Gerwin, Werner and Badorreck, Annika and H{\"u}ttl, Reinhard F.}, title = {Chicken Creek data portal: a web-based R-Shiny platform for terrestrial data repository}, series = {Geophysical Research Abstracts}, volume = {21}, journal = {Geophysical Research Abstracts}, pages = {1}, abstract = {Exploring hydrological and ecological processes plays a key role in understanding ecosystem development. In this respect, the constructed catchment, Chicken Creek (H{\"u}hnerwasser), 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.}, language = {en} } @misc{BadorreckSchaafGerwinetal., author = {Badorreck, Annika and Schaaf, Wolfgang and Gerwin, Werner and Moghadas, Davood and H{\"u}ttl, Reinhard F.}, title = {Long-term monitoring program at the constructed catchment "Chicken Creek"}, series = {Geophysical Research Abstracts}, volume = {21}, journal = {Geophysical Research Abstracts}, pages = {1}, abstract = {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.}, language = {en} } @misc{MoghadasSchaafGerwinetal., author = {Moghadas, Davood and Schaaf, Wolfgang and Gerwin, Werner and Badorreck, Annika and H{\"u}ttl, Reinhard F.}, title = {A web-based platform for terrestrial data repository from Chicken Creek catchment}, series = {Earth Science Informatics}, volume = {12}, journal = {Earth Science Informatics}, number = {4}, issn = {1865-0481}, doi = {10.1007/s12145-019-00385-0}, pages = {671 -- 684}, abstract = {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.}, language = {en} }