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In many regions of the world, snow accumulation and melt constitute important components of the hydrologic cycle. With the objective to improve model performance of the hydrological model WASA-SED (Water Availability in Semi-Arid environments - SEDiments) in catchments affected by snow and ice, a physically-based snow routine has been implemented into the model. The snow routine bases on the energy-balance method of the ECHSE (Eco-hydrological Simulation Environment) software. A first test application has been conducted in two sub-basins of the Isábena river catchment (Central Spanish Pre-Pyrenees). Results were validated using satellite-derived snow cover data. Furthermore, a rainfall gauge correction algorithm to restore the liquid precipitation signal of measurements affected by solid precipitation was applied. The snow module proved to be able to capture the dynamics of the snow cover forming during the cold months of the year. The temporary storage of water in the snow cover is able to improve simulations of river discharge. General patterns of the temporal evolution of observed and simulated snow cover fractions coincide. The work conducted only represents a first step in the process of implementation and evaluation of a physically-based snow routine into WASA-SED. Future work is necessary to further improve and test the snow routine and to resolve difficulties that occurred during model applications in the catchment.
Stable isotopes in precipitation: Modelling intra-event variations using meteorological parameters
(2017)
The short-term variability of the isotopic composition of precipitation in Golm, Germany was assessed and modelled. Isotopic data (D/H and 18O/16O) on intra-event timescales as well as meteorological data from a weather station and a micro rain radar was used. After data preparation and the combination of all three data sets, a multivariate linear regression analysis was conducted. This was done for four different isotopic response variables and for the entire data set as well as for the two subsets Summer and Winter. The used response variables are the δ18O values as the difference to the corresponding event-based mean and as the difference to the median, and the deuterium excess values as the difference to both the mean and the median. The models were evaluated by comparing the modelled values with the observed ones. This showed that the observations could not be reproduced in a satisfactory way. Therefore, several suggestions on how to possibly improve the methods and thus the modelling results are given in the end.
This review analyzes the potential role and long-term effects of field perennial polycultures (mixtures) in agricultural systems, with the aim of reducing the trade-offs between provisioning and regulating ecosystem services. First, crop rotations are identified as a suitable tool for the assessment of the long-term effects of perennial polycultures on ecosystem services, which are not visible at the single-crop level. Second, the ability of perennial polycultures to support ecosystem services when used in crop rotations is quantified through eight agricultural ecosystem services. Legume-grass mixtures and wildflower mixtures are used as examples of perennial polycultures, and compared with silage maize as a typical crop for biomass production. Perennial polycultures enhance soil fertility, soil protection, climate regulation, pollination, pest and weed control, and landscape aesthetics compared with maize. They also score lower for biomass production compared with maize, which confirms the trade-off between provisioning and regulating ecosystem services. However, the additional positive factors provided by perennial polycultures, such as reduced costs for mineral fertilizer, pesticides, and soil tillage, and a significant preceding crop effect that increases the yields of subsequent crops, should be taken into account. However, a full assessment of agricultural ecosystem services requires a more holistic analysis that is beyond the capabilities of current frameworks.
The Tibetan Plateau, the world's largest orogenic plateau, hosts thousands of lakes that play prominent roles as water resources, environmental archives, and sources of natural hazards such as glacier lake outburst floods. Previous studies have reported that the size of lakes on the Tibetan Plateau has changed rapidly in recent years, possibly because of atmospheric warming. Tracking these changes systematically with remote sensing data is challenging given the different spectral signatures of water, the potential for confusing lakes with glaciers, and difficulties in classifying frozen or partly frozen lakes. Object-based image analysis (OBIA) offers new opportunities for automated classification in this context, and we have explored this method for mapping lakes from LANDSAT images and Shuttle Radar Topography Mission (SRTM) elevation data. We tested our algorithm for most of the Tibetan Plateau, where lakes in tectonic depressions or blocked by glaciers and sediments have different surface colours and seasonal ice cover in images obtained in 1995 and 2015. We combined a modified normalised difference water index (MNDWI) with OBIA and local topographic slope data in order to classify lakes with an area > 10 km(2). Our method derived 323 water bodies, with a total area of 31,258 km(2), or 2.6% of the study area (in 2015). The same number of lakes had covered only 24,892 km(2) in 1995; lake area has increased by -26% in the past two decades. The classification had estimated producer's and user's accuracies of 0.98, with a Cohen's kappa and F-score of 0.98, and may thus be a useful approximation for quantifying regional hydrological budgets. We have shown that our method is flexible and transferable to detecting lakes in diverse physical settings on several continents with similar success rates.
Direct anthropogenic influences on the Earth’s subsurface during drilling, extraction or injection activities, can affect land stability by causing subsidence, uplifts or lateral displacements. They can occur in localized as well as in uninhabited and inhabited regions. Thus the associated risks for humans, infrastructure, and environment must be minimized. To achieve this, appropriate surveillance methods must be found that can be used for simultaneous monitoring during such activities. Multi-temporal synthetic aperture radar interferometry (MT-InSAR) methods like the Persistent Scatterer Interferometry (PSI) and the Small BAseline Subsets (SBAS) have been developed as standard approaches for satellite-based surface displacement monitoring. With increasing spatial resolution and availability of SAR sensors in recent years, MT-InSAR can be valuable for the detection and mapping of even the smallest man-made displacements.
This doctoral thesis aims at investigating the capacities of the mentioned standard methods for this purpose, and comprises three main objectives against the backdrop of a user-friendly surveillance service:
(1) the spatial and temporal significance assessment against leveling, (2) the suitability evaluation of PSI and SBAS under different conditions, and (3) the analysis of the link between surface motion and subsurface processes.
Two prominent case studies on anthropogenic induced subsurface processes in Germany serve as the basis for this goal. The first is the distinct urban uplift with severe damages at Staufen im Breisgau that has been associated since 2007 with a failure to implement a shallow geothermal energy supply for an individual building. The second case study considers the pilot project of geological carbon dioxide (CO2) storage at Ketzin, and comprises borehole drilling and fluid injection of more than 67 kt CO2 between 2008 and 2013. Leveling surveys at Staufen and comprehensive background knowledge of the underground processes gained from different kinds of in-situ measurements at both locations deliver a suitable basis for this comparative study and the above stated objectives. The differences in location setting, i.e. urban versus rural site character, were intended to investigate the limitations in the applicability of PSI and SBAS.
For the MT-InSAR analysis, X-band images from the German TerraSAR-X and TanDEM-X satellites were acquired in the standard Stripmap mode with about 3 m spatial resolution in azimuth and range direction. Data acquisition lasted over a period of five years for Staufen (2008-2013), and four years for Ketzin (2009-2013). For the first approximation of the subsurface source, an inversion of the InSAR outcome in Staufen was applied. The modeled uplift based on complex hydromechanical simulations and a correlation analysis with bottomhole pressure data were used for comparison with MT-InSAR measurements at Ketzin.
In response to the defined objectives of this thesis, a higher level of detail can be achieved in mapping surface displacements without in-situ effort by using MT-InSAR in comparison to leveling (1). A clear delineation of the elliptical shaped uplift border and its magnitudes at different parts was possible at Staufen, with the exception of a vegetated area in the northwest. Vegetation coverage and the associated temporal signal decorrelation are the main limitations of MT-InSAR as clearly demonstrated at the Ketzin test site. They result in insufficient measurement point density and unwrapping issues. Therefore, spatial resolutions of one meter or better are recommended to achieve an adequate point density for local displacement analysis and to apply signal noise reduction. Leveling measurements can provide a complementary data source here, but require much effort pertaining to personnel even at the local scale. Horizontal motions could be identified at Staufen by only comparing the temporal evolution of the 1D line of sight (LOS) InSAR measurements with the available leveling data. An exception was the independent LOS decomposition using ascending and descending data sets for the period 2012-2013. The full 3D displacement field representation failed due to insufficient orbit-related, north-south sensitivity of the satellite-based measurements. By using the dense temporal mapping capabilities of the TerraSAR-X/TanDEM-X satellites after every 11 days, the temporal displacement evolution could be captured as good as that with leveling.
With respect to the tested methods and in the view of generality, SBAS should be preferred over PSI (2). SBAS delivered a higher point density, and was therefore less affected by phase unwrapping issues in both case studies. Linking surface motions with subsurface processes is possible when considering simplified geophysical models (3), but it still requires intensive research to gain a deep understanding.
Personal Big Data
(2017)
Many users of cloud-based services are concerned about questions of data privacy. At the same time, they want to benefit from smart data-driven services, which require insight into a person’s individual behaviour. The modus operandi of user modelling is that data is sent to a remote server where the model is constructed and merged with other users’ data. This thesis proposes selective cloud computing, an alternative approach, in which the user model is constructed on the client-side and only an abstracted generalised version of the model is shared with the remote services.
In order to demonstrate the applicability of this approach, the thesis builds an exemplary client-side user modelling technique. As this thesis is carried out in the area of Geoinformatics and spatio-temporal data is particularly sensitive, the application domain for this experiment is the analysis and prediction of a user’s spatio-temporal behaviour.
The user modelling technique is grounded in an innovative conceptual model, which builds upon spatial network theory combined with time-geography. The spatio-temporal constraints of time-geography are applied to the network structure in order to create individual spatio-temporal action spaces. This concept is translated into a novel algorithmic user modelling approach which is solely driven by the user’s own spatio-temporal trajectory data that is generated by the user’s smartphone.
While modern smartphones offer a rich variety of sensory data, this thesis only makes use of spatio-temporal trajectory data, enriched by activity classification, as the input and foundation for the algorithmic model. The algorithmic model consists of three basal components: locations (vertices), trips (edges), and clusters (neighbourhoods).
After preprocessing the incoming trajectory data in order to identify locations, user feedback is used to train an artificial neural network to learn temporal patterns for certain location types (e.g. work, home, bus stop, etc.). This Artificial Neural Network (ANN) is used to automatically detect future location types by their spatio-temporal patterns. The same is done in order to predict the duration of stay at a certain location. Experiments revealed that neural nets were the most successful statistical and machine learning tool to detect those patterns. The location type identification algorithm reached an accuracy of 87.69%, the duration prediction on binned data was less successful and deviated by an average of 0.69 bins. A challenge for the location type classification, as well as for the subsequent components, was the imbalance of trips and connections as well as the low accuracy of the trajectory data. The imbalance is grounded in the fact that most users exhibit strong habitual patterns (e.g. home > work), while other patterns are rather rare by comparison. The accuracy problem derives from the energy-saving location sampling mode, which creates less accurate results.
Those locations are then used to build a network that represents the user’s spatio-temporal behaviour. An initial untrained ANN to predict movement on the network only reached 46% average accuracy. Only lowering the number of included edges, focusing on more common trips, increased the performance. In order to further improve the algorithm, the spatial trajectories were introduced into the predictions. To overcome the accuracy problem, trips between locations were clustered into so-called spatial corridors, which were intersected with the user’s current trajectory. The resulting intersected trips were ranked through a k-nearest-neighbour algorithm. This increased the performance to 56%. In a final step, a combination of a network and spatial clustering algorithm was built in order to create clusters, therein reducing the variety of possible trips. By only predicting the destination cluster instead of the exact location, it is possible to increase the performance to 75% including all classes.
A final set of components shows in two exemplary ways how to deduce additional inferences from the underlying spatio-temporal data. The first example presents a novel concept for predicting the ‘potential memorisation index’ for a certain location. The index is based on a cognitive model which derives the index from the user’s activity data in that area. The second example embeds each location in its urban fabric and thereby enriches its cluster’s metadata by further describing the temporal-semantic activity in an area (e.g. going to restaurants at noon).
The success of the client-side classification and prediction approach, despite the challenges of inaccurate and imbalanced data, supports the claimed benefits of the client-side modelling concept. Since modern data-driven services at some point do need to receive user data, the thesis’ computational model concludes with a concept for applying generalisation to semantic, temporal, and spatial data before sharing it with the remote service in order to comply with the overall goal to improve data privacy. In this context, the potentials of ensemble training (in regards to ANNs) are discussed in order to highlight the potential of only sharing the trained ANN instead of the raw input data.
While the results of our evaluation support the assets of the proposed framework, there are two important downsides of our approach compared to server-side modelling. First, both of these server-side advantages are rooted in the server’s access to multiple users’ data. This allows a remote service to predict spatio-in the user-specific data, which represents the second downside. While minor classes will likely be minor classes in a bigger dataset as well, for each class, there will still be more variety than in the user-specific dataset. The author emphasises that the approach presented in this work holds the potential to change the privacy paradigm in modern data-driven services. Finding combinations of client- and server-side modelling could prove a promising new path for data-driven innovation.
Beyond the technological perspective, throughout the thesis the author also offers a critical view on the data- and technology-driven development of this work. By introducing the client-side modelling with user-specific artificial neural networks, users generate their own algorithm. Those user-specific algorithms are influenced less by generalised biases or developers’ prejudices. Therefore, the user develops a more diverse and individual perspective through his or her user model. This concept picks up the idea of critical cartography, which questions the status quo of how space is perceived and represented.
The functioning of the surface water-groundwater interface as buffer, filter and reactive zone is important for water quality, ecological health and resilience of streams and riparian ecosystems. Solute and heat exchange across this interface is driven by the advection of water. Characterizing the flow conditions in the streambed is challenging as flow patterns are often complex and multidimensional, driven by surface hydraulic gradients and groundwater discharge. This thesis presents the results of an integrated approach of studies, ranging from the acquisition of field data, the development of analytical and numerical approaches to analyse vertical temperature profiles to the detailed, fully-integrated 3D numerical modelling of water and heat flux at the reach scale. All techniques were applied in order to characterize exchange flux between stream and groundwater, hyporheic flow paths and temperature patterns.
The study was conducted at a reach-scale section of the lowland Selke River, characterized by distinctive pool riffle sequences and fluvial islands and gravel bars. Continuous time series of hydraulic heads and temperatures were measured at different depths in the river bank, the hyporheic zone and within the river. The analyses of the measured diurnal temperature variation in riverbed sediments provided detailed information about the exchange flux between river and groundwater. Beyond the one-dimensional vertical water flow in the riverbed sediment, hyporheic and parafluvial flow patterns were identified. Subsurface flow direction and magnitude around fluvial islands and gravel bars at the study site strongly depended on the position around the geomorphological structures and on the river stage. Horizontal water flux in the streambed substantially impacted temperature patterns in the streambed. At locations with substantial horizontal fluxes the penetration depths of daily temperature fluctuations was reduced in comparison to purely vertical exchange conditions.
The calibrated and validated 3D fully-integrated model of reach-scale water and heat fluxes across the river-groundwater interface was able to accurately represent the real system. The magnitude and variations of the simulated temperatures matched the observed ones, with an average mean absolute error of 0.7 °C and an average Nash Sutcliffe Efficiency of 0.87. The simulation results showed that the water and heat exchange at the surface water-groundwater interface is highly variable in space and time with zones of daily temperature oscillations penetrating deep into the sediment and spots of daily constant temperature following the average groundwater temperature. The average hyporheic flow path temperature was found to strongly correlate with the flow path residence time (flow path length) and the temperature gradient between river and groundwater. Despite the complexity of these processes, the simulation results allowed the derivation of a general empirical relationship between the hyporheic residence times and temperature patterns. The presented results improve our understanding of the complex spatial and temporal dynamics of water flux and thermal processes within the shallow streambed. Understanding these links provides a general basis from which to assess hyporheic temperature conditions in river reaches.
Understanding the distribution of species is fundamental for biodiversity conservation, ecosystem management, and increasingly also for climate impact assessment. The presence of a species in a given site depends on physiological limitations (abiotic factors), interactions with other species (biotic factors), migratory or dispersal processes (site accessibility) as well as the continuing
effects of past events, e.g. disturbances (site legacy). Existing approaches to predict species distributions either (i) correlate observed species occurrences with environmental variables describing abiotic limitations, thus ignoring biotic interactions, dispersal and legacy effects (statistical species distribution model, SDM); or (ii) mechanistically model the variety of processes determining species distributions (process-based model, PBM). SDMs are widely used due to their easy applicability and ability to handle varied data qualities. But they fail to reproduce the dynamic response of species distributions to changing conditions. PBMs are expected to be superior in this respect, but they need very specific data unavailable for many species, and are often more complex and require more computational effort. More recently, hybrid models link the two approaches to combine their respective strengths.
In this thesis, I apply and compare statistical and process-based approaches to predict species distributions, and I discuss their respective limitations, specifically for applications in changing environments. Detailed analyses of SDMs for boreal tree species in Finland reveal that nonclimatic predictors - edaphic properties and biotic interactions - are important limitations at the treeline, contesting the assumption of unrestricted, climatically induced range expansion. While the estimated SDMs are successful within their training data range, spatial and temporal model transfer fails. Mapping and comparing sampled predictor space among data subsets identifies spurious extrapolation as the plausible explanation for limited model transferability. Using these findings, I analyze the limited success of an established PBM (LPJ-GUESS) applied to the same problem. Examination of process representation and parameterization in the PBM identifies implemented processes to adjust (competition between species, disturbance) and missing processes that are crucial in boreal forests (nutrient limitation, forest management). Based on climatic correlations shifting over time, I stress the restricted temporal transferability of bioclimatic limits used in LPJ-GUESS and similar PBMs. By critically assessing the performance of SDM and PBM in this application, I demonstrate the importance of understanding the limitations of the
applied methods.
As a potential solution, I add a novel approach to the repertoire of existing hybrid models. By simulation experiments with an individual-based PBM which reproduces community dynamics resulting from biotic factors, dispersal and legacy effects, I assess the resilience of coastal vegetation to abrupt hydrological changes. According to the results of the resilience analysis, I then modify temporal SDM predictions, thereby transferring relevant process detail from PBM to
SDM. The direction of knowledge transfer from PBM to SDM avoids disadvantages of current hybrid models and increases the applicability of the resulting model in long-term, large-scale applications. A further advantage of the proposed framework is its flexibility, as it is readily extended to other model types, disturbance definitions and response characteristics.
Concluding, I argue that we already have a diverse range of promising modelling tools at hand, which can be refined further. But most importantly, they need to be applied more thoughtfully. Bearing their limitations in mind, combining their strengths and openly reporting underlying assumptions and uncertainties is the way forward.
Earth observation data have become an outstanding basis for analyzing environmental
aspects. The increasing availability of remote sensing data is accompanied
by an increasing user demand. Within the scope of the COOPERNICUS-initiative,
the automatic processing of remote sensing data is important for supplying value-
added-information products. The use of additional data like land-water-masks
in the context of deriving value-added information products can stabilize and
improve the product quality of information products.
The authors of this contribution would like to discuss different automated
processing algorithms which are based on land-water masks for value-added
data interpretation. These developments were supported or accompanied by Prof.
Hartmut Asche.
Statistics Canada, Canada’s national statistics agency, offers a suite of spatial
files for mapping and analysis of its various population data products. The following
article showcases possibilities and shortfalls of the existing spatial files
for mapping population data, and provides an overview of the structure of the
available boundary files from the regional to the dissemination block level. Due
to Canada’s highly dispersed population, mapping its distribution and density can
be challenging. Common mapping techniques such as the choropleth method are
suitable only for mapping spatially high resolution data such as data at the dissemination
area level. To allow for mapping of population data at less detailed levels
such as census divisions or provinces, Statistics Canada has created a so-called
ecumene boundary file which outlines the inhabited area of Canada and can be
used to more accurately visualize Canada’s population distribution and density.