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The Adana Basin of southern Turkey, situated at the SE margin of the Central Anatolian Plateau is ideally located to record Neogene topographic and tectonic changes in the easternmost Mediterranean realm. Using industry seismic reflection data we correlate 34 seismic profiles with corresponding exposed units in the Adana Basin. The time-depth conversion of the interpreted seismic profiles allows us to reconstruct the subsidence curve of the Adana Basin and to outline the occurrence of a major increase in both subsidence and sedimentation rates at 5.45 – 5.33 Ma, leading to the deposition of almost 1500 km3 of conglomerates and marls. Our provenance analysis of the conglomerates reveals that most of the sediment is derived from and north of the SE margin of the Central Anatolian Plateau. A comparison of these results with the composition of recent conglomerates and the present drainage basins indicates major changes between late Messinian and present-day source areas. We suggest that these changes in source areas result of uplift and ensuing erosion of the SE margin of the plateau. This hypothesis is supported by the comparison of the Adana Basin subsidence curve with the subsidence curve of the Mut Basin, a mainly Neogene basin located on top of the Central Anatolian Plateau southern margin, showing that the Adana Basin subsidence event is coeval with an uplift episode of the plateau southern margin. The collection of several fault measurements in the Adana region show different deformation styles for the NW and SE margins of the Adana Basin. The weakly seismic NW portion of the basin is characterized by extensional and transtensional structures cutting Neogene deposits, likely accomodating the differential uplift occurring between the basin and the SE margin of the plateau. We interpret the tectonic evolution of the southern flank of the Central Anatolian Plateau and the coeval subsidence and sedimentation in the Adana Basin to be related to deep lithospheric processes, particularly lithospheric delamination and slab break-off.
Several mechanisms are proposed to be part of the earthquake triggering process, including static stress interactions and dynamic stress transfer. Significant differences of these mechanisms are particularly expected in the spatial distribution of aftershocks. However, testing the different hypotheses is challenging because it requires the consideration of the large uncertainties involved in stress calculations as well as the appropriate consideration of secondary aftershock triggering which is related to stress changes induced by smaller pre- and aftershocks. In order to evaluate the forecast capability of different mechanisms, I take the effect of smaller--magnitude earthquakes into account by using the epidemic type aftershock sequence (ETAS) model where the spatial probability distribution of direct aftershocks, if available, is correlated to alternative source information and mechanisms. Surface shaking, rupture geometry, and slip distributions are tested. As an approximation of the shaking level, ShakeMaps are used which are available in near real-time after a mainshock and thus could be used for first-order forecasts of the spatial aftershock distribution. Alternatively, the use of empirical decay laws related to minimum fault distance is tested and Coulomb stress change calculations based on published and random slip models. For comparison, the likelihood values of the different model combinations are analyzed in the case of several well-known aftershock sequences (1992 Landers, 1999 Hector Mine, 2004 Parkfield). The tests show that the fault geometry is the most valuable information for improving aftershock forecasts. Furthermore, they reveal that static stress maps can additionally improve the forecasts of off--fault aftershock locations, while the integration of ground shaking data could not upgrade the results significantly. In the second part of this work, I focused on a procedure to test the information content of inverted slip models. This allows to quantify the information gain if this kind of data is included in aftershock forecasts. For this purpose, the ETAS model based on static stress changes, which is introduced in part one, is applied. The forecast ability of the models is systematically tested for several earthquake sequences and compared to models using random slip distributions. The influence of subfault resolution and segment strike and dip is tested. Some of the tested slip models perform very good, in that cases almost no random slip models are found to perform better. Contrastingly, for some of the published slip models, almost all random slip models perform better than the published slip model. Choosing a different subfault resolution hardly influences the result, as long the general slip pattern is still reproducible. Whereas different strike and dip values strongly influence the results depending on the standard deviation chosen, which is applied in the process of randomly selecting the strike and dip values.
Surface displacement at volcanic edifices is related to subsurface processes associated with magma movements, fluid transfers within the volcano edifice and gravity-driven deformation processes. Understanding of associated ground displacements is of importance for assessment of volcanic hazards. For example, volcanic unrest is often preceded by surface uplift, caused by magma intrusion and followed by subsidence, after the withdrawal of magma. Continuous monitoring of the surface displacement at volcanoes therefore might allow the forecasting of upcoming eruptions to some extent. In geophysics, the measured surface displacements allow the parameters of possible deformation sources to be estimated through analytical or numerical modeling. This is one way to improve the understanding of subsurface processes acting at volcanoes. Although the monitoring of volcanoes has significantly improved in the last decades (in terms of technical advancements and number of monitored volcanoes), the forecasting of volcanic eruptions remains puzzling. In this work I contribute towards the understanding of the subsurface processes at volcanoes and thus to the improvement of volcano eruption forecasting. I have investigated the displacement field of Llaima volcano in Chile and of Tendürek volcano in East Turkey by using synthetic aperture radar interferometry (InSAR). Through modeling of the deformation sources with the extracted displacement data, it was possible to gain insights into potential subsurface processes occurring at these two volcanoes that had been barely studied before. The two volcanoes, although of very different origin, composition and geometry, both show a complexity of interacting deformation sources. At Llaima volcano, the InSAR technique was difficult to apply, due to the large decorrelation of the radar signal between the acquisition of images. I developed a model-based unwrapping scheme, which allows the production of reliable displacement maps at the volcano that I used for deformation source modeling. The modeling results show significant differences in pre- and post-eruptive magmatic deformation source parameters. Therefore, I conjecture that two magma chambers exist below Llaima volcano: a post-eruptive deep one and a shallow one possibly due to the pre-eruptive ascent of magma. Similar reservoir depths at Llaima have been confirmed by independent petrologic studies. These reservoirs are interpreted to be temporally coupled. At Tendürek volcano I have found long-term subsidence of the volcanic edifice, which can be described by a large, magmatic, sill-like source that is subject to cooling contraction. The displacement data in conjunction with high-resolution optical images, however, reveal arcuate fractures at the eastern and western flank of the volcano. These are most likely the surface expressions of concentric ring-faults around the volcanic edifice that show low magnitudes of slip over a long time. This might be an alternative mechanism for the development of large caldera structures, which are so far assumed to be generated during large catastrophic collapse events. To investigate the potential subsurface geometry and relation of the two proposed interacting sources at Tendürek, a sill-like magmatic source and ring-faults, I have performed a more sophisticated numerical modeling approach. The optimum source geometries show, that the size of the sill-like source was overestimated in the simple models and that it is difficult to determine the dip angle of the ring-faults with surface displacement data only. However, considering physical and geological criteria a combination of outward-dipping reverse faults in the west and inward-dipping normal faults in the east seem to be the most likely. Consequently, the underground structure at the Tendürek volcano consists of a small, sill-like, contracting, magmatic source below the western summit crater that causes a trapdoor-like faulting along the ring-faults around the volcanic edifice. Therefore, the magmatic source and the ring-faults are also interpreted to be temporally coupled. In addition, a method for data reduction has been improved. The modeling of subsurface deformation sources requires only a relatively small number of well distributed InSAR observations at the earth’s surface. Satellite radar images, however, consist of several millions of these observations. Therefore, the large amount of data needs to be reduced by several orders of magnitude for source modeling, to save computation time and increase model flexibility. I have introduced a model-based subsampling approach in particular for heterogeneously-distributed observations. It allows a fast calculation of the data error variance-covariance matrix, also supports the modeling of time dependent displacement data and is, therefore, an alternative to existing methods.
Landslides are one of the biggest natural hazards in Georgia, a mountainous country in the Caucasus. So far, no systematic monitoring and analysis of the dynamics of landslides in Georgia has been made. Especially as landslides are triggered by extrinsic processes, the analysis of landslides together with precipitation and earthquakes is challenging. In this thesis I describe the advantages and limits of remote sensing to detect and better understand the nature of landslide in Georgia. The thesis is written in a cumulative form, composing a general introduction, three manuscripts and a summary and outlook chapter. In the present work, I measure the surface displacement due to active landslides with different interferometric synthetic aperture radar (InSAR) methods. The slow landslides (several cm per year) are well detectable with two-pass interferometry. In same time, the extremely slow landslides (several mm per year) could be detected only with time series InSAR techniques. I exemplify the success of InSAR techniques by showing hitherto unknown landslides, located in the central part of Georgia. Both, the landslide extent and displacement rate is quantified. Further, to determine a possible depth and position of potential sliding planes, inverse models were developed. Inverse modeling searches for parameters of source which can create observed displacement distribution. I also empirically estimate the volume of the investigated landslide using displacement distributions as derived from InSAR combined with morphology from an aerial photography. I adapted a volume formula for our case, and also combined available seismicity and precipitation data to analyze potential triggering factors. A governing question was: What causes landslide acceleration as observed in the InSAR data? The investigated area (central Georgia) is seismically highly active. As an additional product of the InSAR data analysis, a deformation area associated with the 7th September Mw=6.0 earthquake was found. Evidences of surface ruptures directly associated with the earthquake could not be found in the field, however, during and after the earthquake new landslides were observed. The thesis highlights that deformation from InSAR may help to map area prone landslides triggering by earthquake, potentially providing a technique that is of relevance for country wide landslide monitoring, especially as new satellite sensors will emerge in the coming years.
Automated location of seismic events is a very important task in microseismic monitoring operations as well for local and regional seismic monitoring. Since microseismic records are generally characterised by low signal-to-noise ratio, such methods are requested to be noise robust and sufficiently accurate. Most of the standard automated location routines are based on the automated picking, identification and association of the first arrivals of P and S waves and on the minimization of the residuals between theoretical and observed arrival times of the considered seismic phases. Although current methods can accurately pick P onsets, the automatic picking of the S onset is still problematic, especially when the P coda overlaps the S wave onset. In this thesis I developed a picking free automated method based on the Short-Term-Average/Long-Term-Average (STA/LTA) traces at different stations as observed data. I used the STA/LTA of several characteristic functions in order to increase the sensitiveness to the P wave and the S waves. For the P phases we use the STA/LTA traces of the vertical energy function, while for the S phases, we use the STA/LTA traces of the horizontal energy trace and then a more optimized characteristic function which is obtained using the principal component analysis technique. The orientation of the horizontal components can be retrieved by robust and linear approach of waveform comparison between stations within a network using seismic sources outside the network (chapter 2). To locate the seismic event, we scan the space of possible hypocentral locations and origin times, and stack the STA/LTA traces along the theoretical arrival time surface for both P and S phases. Iterating this procedure on a three-dimensional grid we retrieve a multidimensional matrix whose absolute maximum corresponds to the spatial and temporal coordinates of the seismic event. Location uncertainties are then estimated by perturbing the STA/LTA parameters (i.e the length of both long and short time windows) and relocating each event several times. In order to test the location method I firstly applied it to a set of 200 synthetic events. Then we applied it to two different real datasets. A first one related to mining induced microseismicity in a coal mine in the northern Germany (chapter 3). In this case we successfully located 391 microseismic event with magnitude range between 0.5 and 2.0 Ml. To further validate the location method I compared the retrieved locations with those obtained by manual picking procedure. The second dataset consist in a pilot application performed in the Campania-Lucania region (southern Italy) using a 33 stations seismic network (Irpinia Seismic Network) with an aperture of about 150 km (chapter 4). We located 196 crustal earthquakes (depth < 20 km) with magnitude range 1.1 < Ml < 2.7. A subset of these locations were compared with accurate locations retrieved by a manual location procedure based on the use of a double difference technique. In both cases results indicate good agreement with manual locations. Moreover, the waveform stacking location method results noise robust and performs better than classical location methods based on the automatic picking of the P and S waves first arrivals.
Logging and large earthquakes are disturbances that may significantly affect hydrological and erosional processes and process rates, although in decisively different ways. Despite numerous studies that have documented the impacts of both deforestation and earthquakes on water and sediment fluxes, a number of details regarding the timing and type of de- and reforestation; seismic impacts on subsurface water fluxes; or the overall geomorphic work involved have remained unresolved. The main objective of this thesis is to address these shortcomings and to better understand and compare the hydrological and erosional process responses to such natural and man-made disturbances. To this end, south-central Chile provides an excellent natural laboratory owing to its high seismicity and the ongoing conversion of land into highly productive plantation forests. In this dissertation I combine paired catchment experiments, data analysis techniques, and physics-based modelling to investigate: 1) the effect of plantation forests on water resources, 2) the source and sink behavior of timber harvest areas in terms of overland flow generation and sediment fluxes, 3) geomorphic work and its efficiency as a function of seasonal logging, 4) possible hydrologic responses of the saturated zone to the 2010 Maule earthquake and 5) responses of the vadose zone to this earthquake. Re 1) In order to quantify the hydrologic impact of plantation forests, it is fundamental to first establish their water balances. I show that tree species is not significant in this regard, i.e. Pinus radiata and Eucalyptus globulus do not trigger any decisive different hydrologic response. Instead, water consumption is more sensitive to soil-water supply for the local hydro-climatic conditions. Re 2) Contradictory opinions exist about whether timber harvest areas (THA) generate or capture overland flow and sediment. Although THAs contribute significantly to hydrology and sediment transport because of their spatial extent, little is known about the hydrological and erosional processes occurring on them. I show that THAs may act as both sources and sinks for overland flow, which in turn intensifies surface erosion. Above a rainfall intensity of ~20 mm/h, which corresponds to <10% of all rainfall, THAs may generate runoff whereas below that threshold they remain sinks. The overall contribution of Hortonian runoff is thus secondary considering the local rainfall regime. The bulk of both runoff and sediment is generated by Dunne, saturation excess, overland flow. I also show that logging may increase infiltrability on THAs which may cause an initial decrease in streamflow followed by an increase after the groundwater storage has been refilled. Re 3) I present changes in frequency-magnitude distributions following seasonal logging by applying Quantile Regression Forests at hitherto unprecedented detail. It is clearly the season that controls the hydro-geomorphic work efficiency of clear cutting. Logging, particularly dry seasonal logging, caused a shift of work efficiency towards less flashy and mere but more frequent moderate rainfall-runoff events. The sediment transport is dominated by Dunne overland flow which is consistent with physics-based modelling using WASA-SED. Re 4) It is well accepted that earthquakes may affect hydrological processes in the saturated zone. Assuming such flow conditions, consolidation of saturated saprolitic material is one possible response. Consolidation raises the hydraulic gradients which may explain the observed increase in discharge following earthquakes. By doing so, squeezed water saturates the soil which in turn increases the water accessible for plant transpiration. Post-seismic enhanced transpiration is reflected in the intensification of diurnal cycling. Re 5) Assuming unsaturated conditions, I present the first evidence that the vadose zone may also respond to seismic waves by releasing pore water which in turn feeds groundwater reservoirs. By doing so, water tables along the valley bottoms are elevated thus providing additional water resources to the riparian vegetation. By inverse modelling, the transient increase in transpiration is found to be 30-60%. Based on the data available, both hypotheses, are not testable. Finally, when comparing the hydrological and erosional effects of the Maule earthquake with the impact of planting exotic plantation forests, the overall observed earthquake effects are comparably small, and limited to short time scales.
The Arctic tundra, covering approx. 5.5 % of the Earth’s land surface, is one of the last ecosystems remaining closest to its untouched condition. Remote sensing is able to provide information at regular time intervals and large spatial scales on the structure and function of Arctic ecosystems. But almost all natural surfaces reveal individual anisotropic reflectance behaviors, which can be described by the bidirectional reflectance distribution function (BRDF). This effect can cause significant changes in the measured surface reflectance depending on solar illumination and sensor viewing geometries. The aim of this thesis is the hyperspectral and spectro-directional reflectance characterization of important Arctic tundra vegetation communities at representative Siberian and Alaskan tundra sites as basis for the extraction of vegetation parameters, and the normalization of BRDF effects in off-nadir and multi-temporal remote sensing data. Moreover, in preparation for the upcoming German EnMAP (Environmental Mapping and Analysis Program) satellite mission, the understanding of BRDF effects in Arctic tundra is essential for the retrieval of high quality, consistent and therefore comparable datasets. The research in this doctoral thesis is based on field spectroscopic and field spectro-goniometric investigations of representative Siberian and Alaskan measurement grids. The first objective of this thesis was the development of a lightweight, transportable, and easily managed field spectro-goniometer system which nevertheless provides reliable spectro-directional data. I developed the Manual Transportable Instrument platform for ground-based Spectro-directional observations (ManTIS). The outcome of the field spectro-radiometrical measurements at the Low Arctic study sites along important environmental gradients (regional climate, soil pH, toposequence, and soil moisture) show that the different plant communities can be distinguished by their nadir-view reflectance spectra. The results especially reveal separation possibilities between the different tundra vegetation communities in the visible (VIS) blue and red wavelength regions. Additionally, the near-infrared (NIR) shoulder and NIR reflectance plateau, despite their relatively low values due to the low structure of tundra vegetation, are still valuable information sources and can separate communities according to their biomass and vegetation structure. In general, all different tundra plant communities show: (i) low maximum NIR reflectance; (ii) a weakly or nonexistent visible green reflectance peak in the VIS spectrum; (iii) a narrow “red-edge” region between the red and NIR wavelength regions; and (iv) no distinct NIR reflectance plateau. These common nadir-view reflectance characteristics are essential for the understanding of the variability of BRDF effects in Arctic tundra. None of the analyzed tundra communities showed an even closely isotropic reflectance behavior. In general, tundra vegetation communities: (i) usually show the highest BRDF effects in the solar principal plane; (ii) usually show the reflectance maximum in the backward viewing directions, and the reflectance minimum in the nadir to forward viewing directions; (iii) usually have a higher degree of reflectance anisotropy in the VIS wavelength region than in the NIR wavelength region; and (iv) show a more bowl-shaped reflectance distribution in longer wavelength bands (>700 nm). The results of the analysis of the influence of high sun zenith angles on the reflectance anisotropy show that with increasing sun zenith angles, the reflectance anisotropy changes to azimuthally symmetrical, bowl-shaped reflectance distributions with the lowest reflectance values in the nadir view position. The spectro-directional analyses also show that remote sensing products such as the NDVI or relative absorption depth products are strongly influenced by BRDF effects, and that the anisotropic characteristics of the remote sensing products can significantly differ from the observed BRDF effects in the original reflectance data. But the results further show that the NDVI can minimize view angle effects relative to the contrary spectro-directional effects in the red and NIR bands. For the researched tundra plant communities, the overall difference of the off-nadir NDVI values compared to the nadir value increases with increasing sensor viewing angles, but on average never exceeds 10 %. In conclusion, this study shows that changes in the illumination-target-viewing geometry directly lead to an altering of the reflectance spectra of Arctic tundra communities according to their object-specific BRDFs. Since the different tundra communities show only small, but nonetheless significant differences in the surface reflectance, it is important to include spectro-directional reflectance characteristics in the algorithm development for remote sensing products.
Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties.
The surface heat flow (qs) is paramount for modeling the thermal structure of the lithosphere. Changes in the qs over a distinct lithospheric unit are normally directly reflecting changes in the crustal composition and therewith the radiogenic heat budget (e.g., Rudnick et al., 1998; Förster and Förster, 2000; Mareschal and Jaupart, 2004; Perry et al., 2006; Hasterok and Chapman, 2011, and references therein) or, less usual, changes in the mantle heat flow (e.g., Pollack and Chapman, 1977). Knowledge of this physical property is therefore of great interest for both academic research and the energy industry. The present study focuses on the qs of central and southern Israel as part of the Sinai Microplate (SM). Having formed during Oligocene to Miocene rifting and break-up of the African and Arabian plates, the SM is characterized by a young and complex tectonic history. Resulting from the time thermal diffusion needs to pass through the lithosphere, on the order of several tens-of-millions of years (e.g., Fowler, 1990); qs-values of the area reflect conditions of pre-Oligocene times. The thermal structure of the lithosphere beneath the SM in general, and south-central Israel in particular, has remained poorly understood. To address this problem, the two parameters needed for the qs determination were investigated. Temperature measurements were made at ten pre-existing oil and water exploration wells, and the thermal conductivity of 240 drill core and outcrop samples was measured in the lab. The thermal conductivity is the sensitive parameter in this determination. Lab measurements were performed on both, dry and water-saturated samples, which is labor- and time-consuming. Another possibility is the measurement of thermal conductivity in dry state and the conversion to a saturated value by using mean model approaches. The availability of a voluminous and diverse dataset of thermal conductivity values in this study allowed (1) in connection with the temperature gradient to calculate new reliable qs values and to use them to model the thermal pattern of the crust in south-central Israel, prior to young tectonic events, and (2) in connection with comparable datasets, controlling the quality of different mean model approaches for indirect determination of bulk thermal conductivity (BTC) of rocks. The reliability of numerically derived BTC values appears to vary between different mean models, and is also strongly dependent upon sample lithology. Yet, correction algorithms may significantly reduce the mismatch between measured and calculated conductivity values based on the different mean models. Furthermore, the dataset allowed the derivation of lithotype-specific conversion equations to calculate the water-saturated BTC directly from data of dry-measured BTC and porosity (e.g., well log derived porosity) with no use of any mean model and thus provide a suitable tool for fast analysis of large datasets. The results of the study indicate that the qs in the study area is significantly higher than previously assumed. The new presented qs values range between 50 and 62 mW m⁻². A weak trend of decreasing heat flow can be identified from the east to the west (55-50 mW m⁻²), and an increase from the Dead Sea Basin to the south (55-62 mW m⁻²). The observed range can be explained by variation in the composition (heat production) of the upper crust, accompanied by more systematic spatial changes in its thickness. The new qs data then can be used, in conjunction with petrophysical data and information on the structure and composition of the lithosphere, to adjust a model of the pre-Oligocene thermal state of the crust in south-central Israel. The 2-D steady-state temperature model was calculated along an E-W traverse based on the DESIRE seismic profile (Mechie et al., 2009). The model comprises the entire lithosphere down to the lithosphere–asthenosphere boundary (LAB) involving the most recent knowledge of the lithosphere in pre-Oligocene time, i.e., prior to the onset of rifting and plume-related lithospheric thermal perturbations. The adjustment of modeled and measured qs allows conclusions about the pre-Oligocene LAB-depth. After the best fitting the most likely depth is 150 km which is consistent with estimations made in comparable regions of the Arabian Shield. It therefore comprises the first ever modelled pre-Oligocene LAB depth, and provides important clues on the thermal state of lithosphere before rifting. This, in turn, is vital for a better understanding of the (thermo)-dynamic processes associated with lithosphere extension and continental break-up.
This cumulative dissertation explored the use of the detection of natural background of fast neutrons, the so-called cosmic-ray neutron sensing (CRS) approach to measure field-scale soil moisture in cropped fields. Primary cosmic rays penetrate the top atmosphere and interact with atmospheric particles. Such interaction results on a cascade of high-energy neutrons, which continue traveling through the atmospheric column. Finally, neutrons penetrate the soil surface and a second cascade is produced with the so-called secondary cosmic-ray neutrons (fast neutrons). Partly, fast neutrons are absorbed by hydrogen (soil moisture). Remaining neutrons scatter back to the atmosphere, where its flux is inversely correlated to the soil moisture content, therefore allowing a non-invasive indirect measurement of soil moisture. The CRS methodology is mainly evaluated based on a field study carried out on a farmland in Potsdam (Brandenburg, Germany) along three crop seasons with corn, sunflower and winter rye; a bare soil period; and two winter periods. Also, field monitoring was carried out in the Schaefertal catchment (Harz, Germany) for long-term testing of CRS against ancillary data. In the first experimental site, the CRS method was calibrated and validated using different approaches of soil moisture measurements. In a period with corn, soil moisture measurement at the local scale was performed at near-surface only, and in subsequent periods (sunflower and winter rye) sensors were placed in three depths (5 cm, 20 cm and 40 cm). The direct transfer of CRS calibration parameters between two vegetation periods led to a large overestimation of soil moisture by the CRS. Part of this soil moisture overestimation was attributed to an underestimation of the CRS observation depth during the corn period ( 5-10 cm), which was later recalculated to values between 20-40 cm in other crop periods (sunflower and winter rye). According to results from these monitoring periods with different crops, vegetation played an important role on the CRS measurements. Water contained also in crop biomass, above and below ground, produces important neutron moderation. This effect was accounted for by a simple model for neutron corrections due to vegetation. It followed crop development and reduced overall CRS soil moisture error for periods of sunflower and winter rye. In Potsdam farmland also inversely-estimated soil hydraulic parameters were determined at the field scale, using CRS soil moisture from the sunflower period. A modelling framework coupling HYDRUS-1D and PEST was applied. Subsequently, field-scale soil hydraulic properties were compared against local scale soil properties (modelling and measurements). Successful results were obtained here, despite large difference in support volume. Simple modelling framework emphasizes future research directions with CRS soil moisture to parameterize field scale models. In Schaefertal catchment, CRS measurements were verified using precipitation and evapotranspiration data. At the monthly resolution, CRS soil water storage was well correlated to these two weather variables. Also clearly, water balance could not be closed due to missing information from other compartments such as groundwater, catchment discharge, etc. In the catchment, the snow influence to natural neutrons was also evaluated. As also observed in Potsdam farmland, CRS signal was strongly influenced by snow fall and snow accumulation. A simple strategy to measure snow was presented for Schaefertal case. Concluding remarks of this dissertation showed that (a) the cosmic-ray neutron sensing (CRS) has a strong potential to provide feasible measurement of mean soil moisture at the field scale in cropped fields; (b) CRS soil moisture is strongly influenced by other environmental water pools such as vegetation and snow, therefore these should be considered in analysis; (c) CRS water storage can be used for soil hydrology modelling for determination of soil hydraulic parameters; and (d) CRS approach has strong potential for long term monitoring of soil moisture and for addressing studies of water balance.