Refine
Document Type
- Article (3)
- Doctoral Thesis (3)
Is part of the Bibliography
- yes (6)
Keywords
- monitoring (6) (remove)
Institute
- Institut für Geowissenschaften (6) (remove)
Understanding the key factors influencing the water quality of large river systems forms an important basis for the assessment and protection of cross-regional ecosystems and the implementation of adapted water management concepts. However, identifying these factors requires in-depth comprehension of the unique environmental systems, which can only be achieved by detailed water quality monitoring.
Within the scope of the joint science and sports event "Elbschwimmstaffel" (swimming relay on the river Elbe) in June/July 2017 organized by the German Ministry of Education and Research, water quality data were acquired along a 550 km long stretch of the Elbe River in Germany. During the survey, eight physiochemical water quality parameters were recorded in high spatial and temporal resolution with the BIOFISH multisensor system. Multivariate statistical methods were applied to identify and delineate processes influencing the water quality.
The BIOFISH dataset revealed that phytoplankton activity has a major impact on the water quality of the Elbe River in the summer months. The results suggest that phytoplankton biomass constitutes a substantial proportion of the suspended particles and that photosynthetic activity of phytoplankton is closely related to significant temporal changes in pH and oxygen saturation.
An evaluation of the BIOFISH data based on the combination of statistical analysis with weather and discharge data shows that the hydrological and meteorological history of the sampled water body was the main driver of phytoplankton dynamics. This study demonstrates the capacity of longitudinal river surveys with the BIOFISH or similar systems for water quality assessment, the identification of pollution sources and their utilization for online in situ monitoring of rivers.
Volcano-seismic signals such as long-period events and tremor are important indicators for volcanic activity and unrest. However, their wavefield is complex and characterization and location using traditional seismological instrumentation is often difficult.
In 2019 we recorded the full seismic wavefield using a newly developed 3C rotational sensor co-located with a 3C traditional seismometer on Etna, Italy. We compare the performance of the rotational sensor, the seismometer and the Istituto Nazionale di Geofisica e Vulcanologia-Osservatorio Etneo (INGV-OE) seismic network with respect to the analysis of complex volcano-seismic signals. We create event catalogs for volcano-tectonic (VT) and long-period (LP) events combining a STA/LTA algorithm and cross-correlations.
The event detection based on the rotational sensor is as reliable as the seismometer-based detection. The LP events are dominated by SH-type waves. Derived SH phase velocities range from 500 to 1,000 m/s for LP events and 300-400 m/s for volcanic tremor. SH-waves compose the tremor during weak volcanic activity and SH- and SV-waves during sustained strombolian activity.
We derive back azimuths using (a) horizontal rotational components and (b) vertical rotation rate and transverse acceleration. The estimated back azimuths are consistent with the INGV-OE event location for (a) VT events with an epicentral distance larger than 3 km and some closer events, (b) LP events and tremor in the main crater area. Measuring the full wavefield we can reliably analyze the back azimuths, phase velocities and wavefield composition for VT, LP events and tremor in regions that are difficult to access such as volcanoes.
Die zerstörungsfreien Prüfungen von Bauwerken mit Hilfe von Ultraschallmessverfahren haben in den letzten Jahren an Bedeutung gewonnen. Durch Ultraschallmessungen können die Geometrien von Bauteilen bestimmt sowie von außen nicht sichtbare Fehler wie Delaminationen und Kiesnester erkannt werden.
Mit neuartigen, in das Betonbauteil eingebetteten Ultraschallprüfköpfen sollen nun Bauwerke dauerhaft auf Veränderungen überprüft werden. Dazu werden Ultraschallsignale direkt im Inneren eines Bauteils erzeugt, was die Möglichkeiten der herkömmlichen Methoden der Bauwerksüberwachung wesentlich erweitert. Ein Ultraschallverfahren könnte mit eingebetteten Prüfköpfen ein Betonbauteil kontinuierlich integral überwachen und damit auch stetig fortschreitende Gefügeänderungen, wie beispielsweise Mikrorisse, registrieren.
Sicherheitsrelevante Bauteile, die nach dem Einbau für Messungen unzugänglich oder mittels Ultraschall, beispielsweise durch zusätzliche Beschichtungen der Oberfläche, nicht prüfbar sind, lassen sich mit eingebetteten Prüfköpfen überwachen. An bereits vorhandenen Bauwerken können die Ultraschallprüfköpfe mithilfe von Bohrlöchern und speziellem Verpressmörtel auch nachträglich in das Bauteil integriert werden. Für Fertigbauteile bieten sich eingebettete Prüfköpfe zur Herstellungskontrolle sowie zur Überwachung der Baudurchführung als Werkzeug der Qualitätssicherung an. Auch die schnelle Schadensanalyse eines Bauwerks nach Naturkatastrophen, wie beispielsweise einem Erdbeben oder einer Flut, ist denkbar.
Durch die gute Ankopplung ermöglichen diese neuartigen Prüfköpfe den Einsatz von empfindlichen Auswertungsmethoden, wie die Kreuzkorrelation, die Coda-Wellen-Interferometrie oder die Amplitudenauswertung, für die Signalanalyse. Bei regelmäßigen Messungen können somit sich anbahnende Schäden eines Bauwerks frühzeitig erkannt werden.
Da die Schädigung eines Bauwerks keine direkt messbare Größe darstellt, erfordert eine eindeutige Schadenserkennung in der Regel die Messung mehrerer physikalischer Größen die geeignet verknüpft werden. Physikalische Größen können sein: Ultraschalllaufzeit, Amplitude des Ultraschallsignals und Umgebungstemperatur. Dazu müssen Korrelationen zwischen dem Zustand des Bauwerks, den Umgebungsbedingungen und den Parametern des gemessenen Ultraschallsignals untersucht werden.
In dieser Arbeit werden die neuartigen Prüfköpfe vorgestellt. Es wird beschrieben, dass sie sich, sowohl in bereits errichtete Betonbauwerke als auch in der Konstruktion befindliche, einbauen lassen. Experimentell wird gezeigt, dass die Prüfköpfe in mehreren Ebenen eingebettet sein können da ihre Abstrahlcharakteristik im Beton nahezu ungerichtet ist. Die Mittenfrequenz von rund 62 kHz ermöglicht Abstände, je nach Betonart und SRV, von mindestens 3 m zwischen Prüfköpfen die als Sender und Empfänger arbeiten. Die Empfindlichkeit der eingebetteten Prüfköpfe gegenüber Veränderungen im Beton wird an Hand von zwei Laborexperimenten gezeigt, einem Drei-Punkt-Biegeversuch und einem Versuch zur Erzeugung von Frost-Tau-Wechsel Schäden. Die Ergebnisse werden mit anderen zerstörungsfreien Prüfverfahren verglichen. Es zeigt sich, dass die Prüfköpfe durch die Anwendung empfindlicher Auswertemethoden, auftretende Risse im Beton detektieren, bevor diese eine Gefahr für das Bauwerk darstellen. Abschließend werden Beispiele von Installation der neuartigen Ultraschallprüfköpfe in realen Bauteilen, zwei Brücken und einem Fundament, gezeigt und basierend auf dort gewonnenen ersten Erfahrungen ein Konzept für die Umsetzung einer Langzeitüberwachung aufgestellt.
The 933 km(2) Bengue catchment in northeastern Brazil is characterized by distinct rainy and dry seasons. Precipitation is stored in variously sized reservoirs, which is essential for the local population. In this study, we used TerraSAR-X SM(HH) data for an one-year monitoring of seasonal changes in the reservoir areas from July 2011 to July 2012. The monitoring was based on acquisitions in the ascending pass direction, complemented by occasional descending-pass images. To detect water surface areas, a histogram analysis followed by a global threshold classification was performed, and the results were validated using in situ GPS data. Distinguishing between small reservoirs and similar looking dark areas was difficult. Therefore, we tested several approaches for identifying misclassified areas. An analysis of the surface area dynamics of the reservoirs indicated high spatial and temporal heterogeneities and a large decrease in the total water surface area of the reservoirs in the catchment by approximately 30% within one year.
Large areas in the humid tropics are currently undergoing land-use change. The decrease of tropical rainforest, which is felled for land clearing and timber production, is countered by increasing areas of tree plantations and secondary forests. These changes are known to affect the regional water cycle as a result of plant-specific water demand and by influencing key soil properties which determine hydrological flow paths. One of these key properties sensitive to land-use change is the saturated hydraulic conductivity (Ks) as it governs vertical percolation of water within the soil profile. Low values of Ks in a certain soil depth can form an impeding layer and lead to perched water tables and the development of predominantly lateral flow paths such as overland flow. These processes can induce nutrient redistribution, erosion and soil degradation and thus affect ecosystem services and human livelihoods. Due to its sensitivity to land-use change, Ks is commonly used to assess the associated changes in hydrological flow paths. The objective of this dissertation was to assess the effect of land-use change on hydrological flow paths by analysing Ks as indicator variable. Sources of Ks variability, their implications for Ks monitoring and the relationship between Ks and near-surface hydrological flow paths in the context of land-use change were studied. The research area was located in central Panama, a country widely experiencing the abovementioned changes in land use. Ks is dependent on both static, soil-inherent properties such as particle size and clay mineralogy and dynamic, land use-dependent properties such as organic carbon content. By conducting a pair of studies with one of these influences held constant in each, the importance of static and dynamic properties for Ks was assessed. Applying a space-for-time approach to sample Ks under secondary forests of different age classes on comparable soils, a recovery of Ks from the former pasture use was shown to require more than eight years. The process was limited to the 0−6 cm sampling depth and showed large variability among replicates. A wavelet analysis of a Ks transect crossing different soil map units under comparable land cover, old-growth tropical rainforest, showed large small-scale variability, which was attributed to biotic influences, as well as a possible but non-conclusive influence of soil types. The two results highlight the importance of dynamic, land use-dependent influences on Ks. Monitoring studies can help to quantify land use-induced change of Ks, but there is a variety of sampling designs which differ in efficiency of estimating mean Ks. A comparative study of four designs and their suitability for Ks monitoring is used to give recommendations about designing a Ks monitoring scheme. Quantifying changes in spatial means of Ks for small catchments with a rotational stratified sampling design did not prove to be more efficient than Simple Random Sampling. The lack of large-scale spatial structure prevented benefits of stratification, and large small-scale variability resulting from local biotic processes and artificial effects of destructive sampling caused a lack of temporal consistency in the re-sampling of locations, which is part of the rotational design. The relationship between Ks and near-surface hydrological flow paths is of critical importance when assessing the consequences of land-use change in the humid tropics. The last part of this dissertation aimed at disclosing spatial relationships between Ks and overland flow as influenced by different land cover types. The effects of Ks on overland-flow generation were spatially variable, different between planar plots and incised flowlines and strongly influenced by land-cover characteristics. A simple comparison of Ks values and rainfall intensities was insufficient to describe the observed pattern of overland flow. Likewise, event flow in the stream was apparently not directly related to overland flow response patterns within the catchments. The study emphasises the importance of combining pedological, hydrological, meteorological and botanical measurements to comprehensively understand the land use-driven change in hydrological flow paths. In summary, Ks proved to be a suitable parameter for assessing the influence of land-use change on soils and hydrological processes. The results illustrated the importance of land cover and spatial variability of Ks for decisions on sampling designs and for interpreting overland-flow generation. As relationships between Ks and overland flow were shown to be complex and dependent on land cover, an interdisciplinary approach is required to comprehensively understand the effects of land-use change on soils and near-surface hydrological flow paths in the humid tropics.
Merapi volcano is one of the most active and dangerous volcanoes of the earth. Located in central part of Java island (Indonesia), even a moderate eruption of Merapi poses a high risk to the highly populated area. Due to the close relationship between the volcanic unrest and the occurrence of seismic events at Mt. Merapi, the monitoring of Merapi's seismicity plays an important role for recognizing major changes in the volcanic activity. An automatic seismic event detection and classification system, which is capable to characterize the actual seismic activity in near real-time, is an important tool which allows the scientists in charge to take immediate decisions during a volcanic crisis. In order to accomplish the task of detecting and classifying volcano-seismic signals automatically in the continuous data streams, a pattern recognition approach has been used. It is based on the method of hidden Markov models (HMM), a technique, which has proven to provide high recognition rates at high confidence levels in classification tasks of similar complexity (e.g. speech recognition). Any pattern recognition system relies on the appropriate representation of the input data in order to allow a reasonable class-decision by means of a mathematical test function. Based on the experiences from seismological observatory practice, a parametrization scheme of the seismic waveform data is derived using robust seismological analysis techniques. The wavefield parameters are summarized into a real-valued feature vector per time step. The time series of this feature vector build the basis for the HMM-based classification system. In order to make use of discrete hidden Markov (DHMM) techniques, the feature vectors are further processed by applying a de-correlating and prewhitening transformation and additional vector quantization. The seismic wavefield is finally represented as a discrete symbol sequence with a finite alphabet. This sequence is subject to a maximum likelihood test against the discrete hidden Markov models, learned from a representative set of training sequences for each seismic event type of interest. A time period from July, 1st to July, 5th, 1998 of rapidly increasing seismic activity prior to the eruptive cycle between July, 10th and July, 19th, 1998 at Merapi volcano is selected for evaluating the performance of this classification approach. Three distinct types of seismic events according to the established classification scheme of the Volcanological Survey of Indonesia (VSI) have been observed during this time period. Shallow volcano-tectonic events VTB (h < 2.5 km), very shallow dome-growth related seismic events MP (h < 1 km) and seismic signals connected to rockfall activity originating from the active lava dome, termed Guguran. The special configuration of the digital seismic station network at Merapi volcano, a combination of small-aperture array deployments surrounding Merapi's summit region, allows the use of array methods to parametrize the continuously recorded seismic wavefield. The individual signal parameters are analyzed to determine their relevance for the discrimination of seismic event classes. For each of the three observed event types a set of DHMMs has been trained using a selected set of seismic events with varying signal to noise ratios and signal durations. Additionally, two sets of discrete hidden Markov models have been derived for the seismic noise, incorporating the fact, that the wavefield properties of the ambient vibrations differ considerably during working hours and night time. A total recognition accuracy of 67% is obtained. The mean false alarm (FA) rate can be given by 41 FA/class/day. However, variations in the recognition capabilities for the individual seismic event classes are significant. Shallow volcano-tectonic signals (VTB) show very distinct wavefield properties and (at least in the selected time period) a stable time pattern of wavefield attributes. The DHMM-based classification performs therefore best for VTB-type events, with almost 89% recognition accuracy and 2 FA/day. Seismic signals of the MP- and Guguran-classes are more difficult to detect and classify. Around 64% of MP-events and 74% of Guguran signals are recognized correctly. The average false alarm rate for MP-events is 87 FA/day, whereas for Guguran signals 33 FA/day are obtained. However, the majority of missed events and false alarms for both MP and Guguran events are due to confusion errors between these two event classes in the recognition process. The confusion of MP and Guguran events is interpreted as being a consequence of the selected parametrization approach for the continuous seismic data streams. The observed patterns of the analyzed wavefield attributes for MP and Guguran events show a significant amount of similarity, thus providing not sufficient discriminative information for the numerical classification. The similarity of wavefield parameters obtained for seismic events of MP and Guguran type reflect the commonly observed dominance of path effects on the seismic wave propagation in volcanic environments. The recognition rates obtained for the five-day period of increasing seismicity show, that the presented DHMM-based automatic classification system is a promising approach for the difficult task of classifying volcano-seismic signals. Compared to standard signal detection algorithms, the most significant advantage of the discussed technique is, that the entire seismogram is detected and classified in a single step.