Abstract. Geophysical methods are often used to characterise and monitor the subsurface composition of permafrost. The resolution capacity of standard methods, i.e. Electrical Resistivity Tomography and Refraction Seismic Tomography, depends hereby not only on static parameters such as measurement geometry, but also on the temporal variability in the contrast of the geophysical variables (electrical resistivity and P-wave velocity). Our study analyses the resolution capacity of Electrical Resistivity Tomography and Refraction Seismic Tomography for typical processes in the context of permafrost degradation using synthetic and field data sets of mountain permafrost terrain. In addition, we tested especially the resolution capacity of a petrophysically-based quantitative combination of both methods, the so-called 4-phase model, and by this analysed the expected changes in water and ice content upon permafrost thaw. The results from the synthetic data experiments suggest a higher sensitivity regarding increasing water content compared to decreased ice content, and potentially larger uncertainty for the individual geophysical methods than for the combined evaluation with the 4-phase model. In the latter, ground ice loss can be detected quite reliably, whereas artefacts occur in the case of increased horizontal or vertical water flow. Analysis of field data from a well-investigated rock glacier in the Swiss Alps successfully visualised the seasonal ice loss in summer, and the complex spatially variable ice-, water- and air content changes in an interannual comparison.
Abstract. Geophysical methods are often used to characterize and monitor the subsurface composition of permafrost. The resolution capacity of standard methods, i.e. electrical resistivity tomography and refraction seismic tomography, depends not only on static parameters such as measurement geometry, but also on the temporal variability in the contrast of the geophysical target variables (electrical resistivity and P-wave velocity). Our study analyses the resolution capacity of electrical resistivity tomography and refraction seismic tomography for typical processes in the context of permafrost degradation using synthetic and field data sets of mountain permafrost terrain. In addition, we tested the resolution capacity of a petrophysically based quantitative combination of both methods, the so-called 4-phase model, and through this analysed the expected changes in water and ice content upon permafrost thaw. The results from the synthetic data experiments suggest a higher sensitivity regarding an increase in water content compared to a decrease in ice content. A potentially larger uncertainty originates from the individual geophysical methods than from the combined evaluation with the 4-phase model. In the latter, a loss of ground ice can be detected quite reliably, whereas artefacts occur in the case of increased horizontal or vertical water flow. Analysis of field data from a well-investigated rock glacier in the Swiss Alps successfully visualized the seasonal ice loss in summer and the complex spatially variable ice, water and air content changes in an interannual comparison.
Abstract. In the last decade, agent-based modelling (ABM) became a popular modelling technique in social sciences, medicine, biology, and ecology. ABM was designed to simulate systems that are highly dynamic and sensitive to small variations in their composition and their state. As hydrological systems, and natural systems in general, often show dynamic and non-linear behaviour, ABM can be an appropriate way to model these systems. Nevertheless, only a few studies have utilized the ABM method for process-based modelling in hydrology. The percolation of water through the unsaturated soil is highly responsive to the current state of the soil system; small variations in composition lead to major changes in the transport system. Hence, we present a new approach for modelling the movement of water through a soil column: autonomous water agents that transport water through the soil while interacting with their environment as well as with other agents under physical laws.
Can machine learning effectively lower the effort necessary to extract important information from raw data for hydrological research questions? On the example of a typical water-management task, the extraction of direct runoff flood events from continuous hydrographs, we demonstrate how machine learning can be used to automate the application of expert knowledge to big data sets and extract the relevant information. In particular, we tested seven different algorithms to detect event beginning and end solely from a given excerpt from the continuous hydrograph. First, the number of required data points within the excerpts as well as the amount of training data has been determined. In a local application, we were able to show that all applied Machine learning algorithms were capable to reproduce manually defined event boundaries. Automatically delineated events were afflicted with a relative duration error of 20\% and 5\% event volume. Moreover, we could show that hydrograph separation patterns could easily be learned by the algorithms and are regionally and trans-regionally transferable without significant performance loss. Hence, the training data sets can be very small and trained algorithms can be applied to new catchments lacking training data. The results showed the great potential of machine learning to extract relevant information efficiently and, hence, lower the effort for data preprocessing for water management studies. Moreover, the transferability of trained algorithms to other catchments is a clear advantage to common methods.
Abstract Karstic groundwater systems are often investigated by a combination of environmental or artificial tracers. One of the major downsides of tracer‐based methods is the limited availability of tracer measurements, especially in data sparse regions. This study presents an approach to systematically evaluate the information content of the available data, to interpret predictions of tracer concentration from machine learning algorithms, and to compare different machine learning algorithms to obtain an objective assessment of their applicability for predicting environmental tracers. There is a large variety of machine learning approaches, but no clear rules exist on which of them to use for this specific problem. In this study, we formulated a framework to choose the appropriate algorithm for this purpose. We compared four different well‐established machine learning algorithms (Support Vector Machines, Extreme Learning Machines, Decision Trees, and Artificial Neural Networks) in seven different karst springs in France for their capability to predict tracer concentrations, in this case SO 4 2− and NO 3 − , from discharge. Our study reveals that the machine learning algorithms are able to predict some characteristics of the tracer concentration, but not the whole variance, which is caused by the limited information content in the discharge data. Nevertheless, discharge is often the only information available for a catchment, so the ability to predict at least some characteristics of the tracer concentrations from discharge time series to fill, for example, gaps or increase the database for consecutive analyses is a helpful application of machine learning in data sparse regions or for historic databases.
The newly developed image interpretation approach of agent-based image classification combines the advantages of object-based image classification and expert knowledge. Agent-based classification identifies meaningful objects by autonomous software units that alter their spatial extent and composition to adapt to a changing environment and data availability. Agents deliver highly variable classification results of a remotely sensed scene. Although the approach has proven its general ability, the use of agent-based image classification studies is sparse. With this study, we want to introduce this concept to water resource management in form of the detection of irrigated agriculture. In this study, we present the fundament of an agent-based image classification framework in terms of agriculture and irrigation management that shows promising results. In contrast to pixel-based classification approaches, the agent-based classification uses the shape and the relation of an image object to other image objects to improve classification results. To incorporate the possibility of erroneous classification due to threshold behaviour, a strict and a soft formulation for class membership are applied. The results show that the object- and agent-based approaches both deliver similar results as a traditional pixel-wise classification approach, but improve the completeness of the classes. In this case study, the different formulations have only little influence on the general results, but remain a promising addition to the classification approach. The here presented classification strategy is sensitive to changes in the underlying classification scheme. Nevertheless, this framework is an ideal addition to the toolset of image interpretation especially in natural systems that are affected by human behaviour like irrigated agriculture.
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