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The thickness integrated dense flow avalanche simulation module com1DFA of the open source framework AvaFrame is used for snow avalanche simulations with application in hazard mapping for different mountainous areas. In order to further increase the information value gained from the avalanche simulation results in a global coordinate system, we introduce a thalweg following coordinate system. It allows us to quantitatively compare simulation scenarios and results of different modelling approaches in a new way. It helps to bridge the gap between the modules operating in three-dimensional terrain (com1DFA) versus two-dimensional along the avalanche path, such as the well-known alpha-beta model implemented in module com2AB. One essential step of the analysis procedures (analysis modules in AvaFrame) is the avalanche thalweg generation itself. The thalweg depends on the main flow direction, a property of the avalanche event which is strongly influenced by the terrain the avalanche flow will encounter. So far, the main flow direction is usually derived from observations or avalanche simulations, and the thalweg is generated manually. However, the reproducibility of this method raises an issue, and manually identifying the avalanche thalweg for every slope is unnecessarily time-consuming. In this work, we use com1DFA simulations in three dimensional terrain. We automatically generate the two-dimensional avalanche thalweg by extracting the centre of mass coordinates at every time step. Projecting the simulation results into this thalweg following coordinate system, we can derive the position of the avalanche front and the local travel angles, from which scalar measures like runout length and runout angle are determined. We combine temporal and spatial information by introducing the thalweg-time and thalweg-altitude diagrams. These offer a different perspective on the simulation results and, at a glance, provide information on the evolution of spatio-temporal flow variables (thickness, velocity) along the avalanche thalweg in a single plot. Additionally, by using a numerical particle-grid method, we can evaluate simulation outputs at a particle level and relate them to the whole avalanche flow. Another advantage of the analysis tools operating in the thalweg coordinate system is the possibility to compare simulation results with field measurements. For example, we present in-flow particle sensors trajectories and corresponding velocities recorded during field experiments to evaluate com1DFA simulation results and thereby help to improve the dense flow module. For different avalanche simulations, we show how these analysis modules provide a new way to summarize the complex spatio-temporal flow variables evolution in three dimensional terrain in a more intuitive two dimensional illustration along the automatically generated thalweg.

Groundwater resources are heavily exploited to supply domestic, industrial and agricultural water consumption. Climate and societal changes and associated higher abstraction will alter the subsurface storage in terms of quantity and quality in currently unpredictable ways. In order to ensure sustainable groundwater management, we must evaluate the intrinsic and spatially variable vulnerability of aquifers in terms of water quality issues and the resilience of groundwater volumes to external perturbations such as severe droughts in connection with intensive irrigation. For this purpose, physically based numerical groundwater models are of great importance, especially on the regional scale. The equations applied in these models must be fed with the hydrogeological parameters: The transmissivity T and the storativity S.Both parameters are typically obtained through time consuming and cost intensive hydrogeological in-situ tests or by laboratory analysis of core samples from point information (drillings and wells), resulting in parameters with limited transferability to regional settings. Instead, we propose to determine the parameters by spectral analysis of groundwater level fluctuations using (semi-)analytical solutions for the frequency domain. We developed a fully automatized workflow, taking groundwater level and recharge time series together with little information about the geometry of the aquifer to derive T and S as well as tc (the characteristic response time). While the first two will be used for hydrogeological modelling, the latter can serve as an indication to assess the resilience of the groundwater system directly without additional modelling attempts. The methodology was tested with great success in simplified numerical environments and was applied to real groundwater time series in southern Germany. The response times and the storativities could be robustly estimated while the transmissivities inherit quantifiable uncertainties. Depending on the hydrogeological regime, the parameters represented effective and regional estimates.

Simulation tools are important to investigate and predict mobility and the destructive potential of gravitational mass flows (e.g. snow avalanches). AvaFrame – the open avalanche framework – offers well established computational modeling approaches, tools for data handling and analysis as well as ready to use modules for evaluation and testing. This paper presents the theoretical background, derivation and model verification for one of AvaFrame’s core modules, the thickness integrated computational model for dense flow avalanches, named com1DFA. Particular emphasis within the description of the utilized numerical particle grid method is given to the computation of spatial gradients and the accurate implementation of driving and resisting forces. The implemented method allows to provide a time-space criterion connecting the numerical particles, grid and time discretization. The convergence and robustness of the numerical implementation is checked with respect to the spatio-temporal evolution of the flow variables using tests with a known analytical solution. In addition we present a new test for verifying the accuracy of the numerical simulation in terms of runout (angle and distance). This test is derived from the total energy balance along the center of mass path of the avalanche. This manuscript, particularly in combination with the code availability (open-source code repository) and detailed online documentation provides a description of an extendable framework for modeling and verification of avalanche simulation tools.

First approaches to analyze the propagation of hazardous, extremely rapid, landslides go back at least to the 1930s, when data-driven approaches such as angles of reach were used to estimate runout distances. Various much more sophisticated tools for the simulation of flow-type landsides including debris flows or rock avalanches, or cascading effects involving more than one type of phenomenon, have been developed since then, particularly throughout the last two decades. They build on increasingly complex physical-mathematical models, starting from comparatively simple and straightforward, depth-averaged Voellmy-type mixture models, moving to still depth-averaged two- and three-phase models able to simulate the interaction of landslides with water bodies, and currently proceeding to highly complex and highly flexible full 3D models. Phenomena such as erosion, entrainment, deposition, phase separation, or non-hydrostatic effects are increasingly well understood and incorporated into operational mass flow simulation tools.However, this trend of increasing model complexity, supported by increasing physical process understanding and enhanced computational capacities, is not necessarily in line with the demand by natural hazard practitioners, who need capable but easy-to-handle simulation tools for their work. Besides the computational costs, it is mainly the multitude of often unknown, rather conceptual model parameters representing a barrier for practitioners towards using the more or even the most advanced approaches. Further, more complex models do not necessarily provide more accurate results than simpler ones, this always depends on the scope, purpose, and phenomenon. If only estimates of runout distances or impact areas are needed, very simple data-driven models may do the work. Even when it comes to flow thicknesses, velocities, or impact pressures, ordinary debris flows may still be better represented by comparatively simple mixture models than by parameter-hungry two- or three-phase models. Yet, more complex models are needed for more complex processes such as landslide-lake interactions or other types of process chains with dynamically changing material composition. Therefore, intelligent approaches have to be found to find an appropriate balance between simplicity and complexity.Mainly based on seven years of experience with the depth-averaged multi-phase mass flow simulation framework r.avaflow, we will discuss the main challenges and ideas to find a useful balance between simplicity and complexity in the simulation of landslide runout.

At the core of many avalanche simulation tools, numerical kernels are utilized to solve flow model equations. Aside from trying to fit the models as best as possible to the current understanding of actual flow mechanisms, these kernels have to fulfill general mathematical requirements, such as convergence, stability and consistency. The precision of numerical solutions is limited and needs to be determined by appropriate uncertainty quantification approaches. It is also necessary to assess the impact of input variability propagating through the numerical kernel.To allow kernel testing and uncertainty quantification, the AvaFrame framework provides a suite of test cases as well as analysis tools. This includes tests with known solutions usable to determine the kernel errors (ana1Tests) and idealized/real world topographies to estimate effects of varying simulation setups. By changing numerical settings, flow model setup or input data it is possible to show their effects on simulation results in a quantitative manner. It therefore allows us to relate input variations to the uncertainty in simulation results. Error and uncertainty quantification is done using modules for computing statistical measures (ana4Stats), indicators along an avalanche path (ana3AIMEC) and various visualization routines.We showcase this for our com1DFA dynamical dense flow avalanche (DFA) module. The kernel of com1DFA is based on depth integrated governing equations (shallow water) and solved numerically using the smoothed particle hydrodynamics (SPH) method. Applying our analysis tools, we evaluate the convergence of the DFA kernel with regard to the numerical parameters time step, SPH kernel size and particles size. We investigate the accuracy and precision of the numerical solution using the similarity solution test, a test with a semi-analytic solution for depth integrated equations. It allows us to establish a suitable relation between time step, SPH kernel size and particles size for the com1DFA kernel.Using the same approach for an avalanche setup, we can also vary selected input parameters like friction coefficients and/or release thickness and quantify the resulting uncertainties on simulation results, e.g. runout and peak flow variables.

The dynamics of snow avalanches and in particular their rheology is of big importance to develop improved avalanche models and thus increase safety in mountainous areas. Existing measurement systems only allow a limited in situ view of the dynamics of snow avalanches and therefore demand the development of innovative measurement systems. Furthermore, due to the limited measurement capability of existing systems, comprehensible motion reconstruction is currently not possible. Therefore, the aim of this work is to present a measurement system that enables accurate in flow observations of snow avalanches and has the mechanical properties of a typical snow granule. A main objective of the measurement system is to allow a full motion reconstruction regarding translations and rotations with a high sampling rate and without exceeding sensor ranges.The newly developed system, denoted as AvaNode, has the shape of a concave cube with a variable density to fit typical snow granules in flowing avalanches and their deposits. The AvaNode contains a strapdown inertial navigation sensor capable of measuring accelerations, angular velocities, and magnetic flux densities with up to 400Hz and allows for an estimation of the orientations, velocities, and positions of the AvaNode using state of the art motion reconstruction algorithms. The reconstruction is significantly improved due to precise calibration of all sensors using reference measurements with a 6R robot and onsite magnetic field calibration. In order to get a refined motion trajectory, the AvaNodes are also equipped with radio ranging modules. These modules allow performing time of flight (TOF) measurements, determining the distance between several nodes. A Global Navigation Satellite System (GNSS) module determines longitude, latitude, and altitude, as well as world time, however, with low frequency resolution and larger errors due to snow coverage. To measure the temperature evolution in avalanches, an infrared temperature sensor is attached. Multiple recovery systems like Recco rescue reflector (passive), Pieps TX600 (active), and Lambda4 Smilla (active) are integrated to allow fast retrieval of the sensors.As first results, we present the employed sensor calibration approaches for the inertial navigation with corresponding laboratory data signatures. The sensor calibration allows in-depth analysis of motion data, identifying typical data signatures observed in avalanches. Furthermore, we show first data acquired from in-flow snow avalanche measurements, which prove the functionality of the system and allow the first insights into trajectories of snow granules, regarding accelerations, angular velocities, rotations, and position.

Forests cover large parts of mountain areas. It is therefore necessary to include their effects in simulations at the regional scale to understand the key role forests have for risk mitigation. Process-based physical models can be used for such simulations, but they often require larger computational resources than statistical models. Flow-Py is a customizable, open-source simulation tool to predict the runout and intensity of gravitational mass flows (GMF). Flow-Py is based on data-driven empirical modeling ideas with automated path identification to solve the routing and stopping of GMFs in three-dimensional terrain, requiring fewer parameters than physical GMF runout models. Here we present the custom-built forest plug-in to the Flow-Py simulation tool which accounts for forest effects in the transit and runout zones of snow avalanches. Flow-Py employs the well-known runout angle (α) concept to determine the stopping of a GMF, and routing algorithm consisting of a terrain contribution and persistence contribution. The interaction between forest and avalanches, which can reduce their runout and decrease their intensity can be broken down into two main processes, 1) adding friction and 2) reducing flowing mass or the detrainment of snow. The forest plug-in has the capability to mimic these physical interactions by increasing the runout angle and adjusting the routing flux in forested areas. We present the framework of the forest plug-in for a test case and the results of a sensitivity study on parameters controlling the forest-avalanche interaction. The forest plug-in requires the spatial extent of the forest and an estimate of the kinetic energy of the avalanche to compute the forest’s effect on the avalanche movement. Additional information on the structure of the forest (e.g., forest type, stem density, canopy cover, basal area) can be used to amplify or dampen these effects. The forest information is summarized in the forest structure index (FSI), which indicates how developed a forest is with regards to its optimal protective effect against snow avalanches and ranges between 0 (no protection) and 1 (optimal protection), considering, e.g., dominant forest type, elevation band, or the forest development stage. Forests located in the starting zones of avalanches have long been used as an efficient mitigation measure to reduce avalanche risk. However, forests located in the transit and runout zones of avalanches also have mitigating properties, but the degree of protection is difficult to quantify without simulation tools and their integrated models. Including forest-avalanche interactions in regional-scale simulations with Flow-Py and its forest plug-in allows to estimate the degree to which forest protects human activity and infrastructure against potential avalanches. That is, by combining simulation results with and without forest it is possible to estimate the forest impact, i.e., how much the forest reduces the magnitude (runout and intensity) of the avalanche. Such regional overviews can be calculated fast with large-scale input data, which is important to, e.g., quantify changes in the protective effect of a forest area caused by disturbance agents such as wind, bark beetles or fire.

A detailed knowledge of avalanche dynamics is crucial to optimize flow models that allow avalanche simulation tools to be effectively used for dimensioning mitigation measures or identifying endangered terrain. There are different ways to observe the dynamics in an avalanche during the flow. It can be achieved with remote sensing approaches or fixed sensor systems that interact with the flow. In this Abstract we introduce an inflow sensor system, the so called AvaNodes that are equipped with a variety of sensors, investigating the potential of Global Navigation Satellite System (GNSS) modules.The AvaNode is a cube with 16 cm side length. It is designed to flow in the avalanche and obtain GNSS position and velocity, inertial measurement unit (IMU) based accelerations, angular velocities and the magnetic flux densities, and temperature by means of an infrared thermometer.The utilized GNSS modules are from the ublox CAM-M8 series, that have a position accuracy of 2 m and velocity accuracy of 0.05 m/s, according to the datasheet.To estimate the position accuracy of the AvaNode while covered with snow, experiments were performed with the AvaNode buried in snow at different depths at a known location. Results show that the position accuracy is highly dependent on the number of satellites that the module currently tracks, ranging between 2 and 10 meters. To estimate the GNSS velocity accuracy while the AvaNode is covered with snow, a dynamic experiment with moving sensors was performed. The AvaNode was transported on a sledge while it was buried in 10 and 20 cm of snow. An accuracy in the range of 0.5 m/s was observed, allowing to potentially investigate the dynamics in real avalanches. The influence of burial or snow cover depth did not show conclusive influence on the results and requires further investigation. In 2021 this inflow sensor system was used in two avalanche experiments, on March 15 and 16, obtaining start and end positions, as well as promising GNSS velocities. On March 15 one AvaNode was transported by an avalanche, where the GNSS velocity shows a maximum of 15 m/s and a duration of 50 seconds of the avalanche. On March 16 two AvaNodes were picked up by an avalanche, both showing similar velocity distributions, with a maximum velocity of 17 and 13 m/s.

GIS-based open-source simulation tools for extremely rapid mass flow processes such as snow avalanches, rock avalanches, or debris flows are readily available, covering a broad range of complexity levels – e.g., from single-phase to multi-phase. However, these tools are not suitable for slower types of mass flows characterized by high viscosities. The conventionally used momentum balance equations for rapid flows often appear numerically unstable for high viscosities, leading to the immediate reversion of flow direction or stopping, without appropriate numerical treatment. GIS-based simulation efforts of slow geomorphic flows are reported in the literature, and open source tools are available for specific phenomena such as glaciers, but no comprehensive and readily usable simulation tools have been proposed yet.We present a simple depth-averaged model implementation for the simulation of slow geomorphic flows, including glaciers, rock glaciers, highly viscous lava flows, and those flow-type landslides not classified as extremely or very rapid. Thereby, we use an equilibrium-of-motion concept. For each time step, flow momentum and velocity are computed as the equilibrium between accelerating gravitational forces and decelerating viscous forces, also including a simple law for basal sliding. Momentum balances are not carried over from one time step to the next, meaning that inertial forces, which are not important for slow-moving mass flows, are neglected. Whereas these basic principles are applied to all relevant processes, there is flexibility with regard to the details of model formulation and parameterization: e.g., the well-established shallow-ice approximation can be used to simulate glacier flow.The model is implemented with the GRASS GIS-based open-source mass flow simulation framework r.avaflow and demonstrated on four case studies: an earth flow, the growth of a lava dome, a rock glacier, and a glacier (considering accumulation and ablation). All four processes were reproduced in a plausible way. However, parameterization remains a challenge due to spatio-temporal changes and temperature dependency of viscosity and basal sliding. Our model and its implementation open up new possibilities for climate change impact studies, natural hazard analysis, and environmental education.

Models and simulation tools for gravitational mass flows (GMFs) such as snow avalanches, rockfall, landslides, and debris flows are important for research, education, and practice. In addition to basic simulations and classic applications (e.g., hazard zone mapping), the importance and adaptability of GMF simulation tools for new and advanced applications (e.g., automatic classification of terrain susceptible for GMF initiation or identification of forests with a protective function) are currently driving model developments. In principle, two types of modeling approaches exist: process-based physically motivated and data-based empirically motivated models. The choice for one or the other modeling approach depends on the addressed question, the availability of input data, the required accuracy of the simulation output, and the applied spatial scale. Here we present the computationally inexpensive open-source GMF simulation tool Flow-Py. Flow-Py's model equations are implemented via the Python computer language and based on geometrical relations motivated by the classical data-based runout angle concepts and path routing in three-dimensional terrain. That is, Flow-Py employs a data-based modeling approach to identify process areas and corresponding intensities of GMFs by combining models for routing and stopping, which depend on local terrain and prior movement. The only required input data are a digital elevation model, the positions of starting zones, and a minimum of four model parameters. In addition to the major advantage that the open-source code is freely available for further model development, we illustrate and discuss Flow-Py's key advancements and simulation performance by means of three computational experiments. Implementation and validation. We provide a well-organized and easily adaptable solver and present its application to GMFs on generic topographies. Performance. Flow-Py's performance and low computation time are demonstrated by applying the simulation tool to a case study of snow avalanche modeling on a regional scale. Modularity and expandability. The modular and adaptive Flow-Py development environment allows access to spatial information easily and consistently, which enables, e.g., back-tracking of GMF paths that interact with obstacles to their starting zones. The aim of this contribution is to enable the reader to reproduce and understand the basic concepts of GMF modeling at the level of (1) derivation of model equations and (2) their implementation in the Flow-Py code. Therefore, Flow-Py is an educational, innovative GMF simulation tool that can be applied for basic simulations but also for more sophisticated and custom applications such as identifying forests with a protective function or quantifying effects of forests on snow avalanches, rockfall, landslides, and debris flows.

Testing and benchmarking avalanche models is a crucial step in developing models as well as assessing their applicability. This is not only limited to the representation of physical processes within models, be it via first principles or using empirical relationships, but also concerns their computing environment, including compilers, hardware used, programming language, among others. Test, benchmarking, and comparison strategies can aim at different issues, among others: numerics, the implementation thereof, plausibility, verification, or evaluation. However, they always require reference or expected results. References can come from observations, analytical results, comparison to other models, known physical processes or material properties that cannot be changed – e.g., “avalanches cannot fly”. The question is: which characteristics or properties do we test and how to design appropriate tests? To facilitate this, as part of the newly developed opensource avalanche framework - AvaFrame -, we started providing commonly accessible tools to make testing and developing easier. This ranges from tools to import data, generate input parameters to automatic analysis and plotting. Not only do we provide the infrastructure for testing, but we also provide a set of test cases complete with all necessary input data, reference results, and run script examples. These tests so far include idealized (generic) topographies, specific test cases for numerical questions, and 6 real world avalanches with distinct characteristics. In this contribution we present this freely available set of tests and benchmarks suitable to assess various aspects and properties of a shallow water model solver for a dense flow avalanche model, one of the core computing modules of AvaFrame (com1DFA). We highlight how we utilize the entire range of tests in our continuous model development to assure the quality and applicability / validity of our development. Showing results from comparison to existing models, but also how to extend and apply our strategies to other models or research questions, we invite other researchers and developers to make full use of these tools.

Radar imaging has become increasingly important in either the detection of avalanches or in the scientific characterization of the avalanche flow. Detection radars usually base on the Doppler radar principle that is sensitive to the velocity of moving object but lack sufficient resolution which can be circumvented with frequency-modulated pulsed radars. We present a new radar device as a follow up of the successful GEODAR radar that suit the need for both applications – low resolution detection and high resolution observation. While the original GEODAR is permanently installed in the full-scale avalanche Test site “Vallée de la Sionne" in Valais, Switzerland, our new device is much smaller and can be deployed in fast response to the metrological forecast and avalanche situation. Currently the mGEODAR is installed at Nordkette ski resort above Innsbruck that is known for its frequent artificial avalanche releases. The new radar features a versatile frequency generation scheme using direct digital synthesis and can be quickly reprogrammed into a low-resolution detection mode for continuous data recording that switches to a high-resolution observation mode as soon as an avalanche is detected.Beside the radar system itself, avalanche data are presented of the winter season 2020/21. A focus is on small to medium sized avalanches that are just on the limit to develop into a powder snow avalanche which is characterized by surging in the intermittent frontal region. Connecting the radar data of the dynamic flow evolution with snow conditions will lead to drivers of this flow regime transition. The snow conditions are taken from nearby weather stations and manual snow profiles but also from the radar itself. A continuous scanning of the resting snow cover in the avalanche path during the season further allows snow cover monitoring. This application allows to identify new snow fall, warming and wetting of surface layers as well as diurnal melt-freeze cycles.The radar data should be consequently used for model validation, calibration, and development. With the current measurement campaign of small to mid-sized avalanches, we hope to close a gap in data availability for those events as they are increasingly the object of simulation scenarios, but current modelling and simulation tools are usually calibrated for larger and extreme events. In the future, we expect the mGEODAR radar to be deploy on other gravitational mass-movements phenomena like soil slides and rock-fall.

Earth System Models (ESM) got much more demanding over the last years. Modelled processes got more complex and more and more processes are considered in models. In addition resolutions of the models got higher to improve accuracy of predictions. This requires faster high performance computers (HPC) and better I/O performance. One way to improve I/O performance is to use faster file systems. Last year we showed the impact of the ad-hoc file system on the performance of the ESM EMAC. An ad-hoc file system is a private parallel file system which is created on-demand for an HPC job using the node-local storage devices, in our case solid-state-disks (SSD). It only exists during the runtime of the job. Therefore output data have to be moved to a permanent file system before the job has finished. Performance improvements are due to the use of SSDs in case of small chunks of I/O or a high amount of I/O operations per second. Another reason for a performace boost is because the running job can exclusively access the file system. To get a better overview in which cases ESMs benefit from using ad-hoc file systems we repeated our performance tests with further ESMs with different I/O strategies. In total we now analyzed EMAC (parallel netcdf), ICON2.5 (netcdf with asynchronous I/O), ICON2.6 (netcdf with Climate Data Interface (CDI) library) and OpenGeoSys (parallel VTU).

The STIMTEC and STIMTEC-X hydraulic stimulation experiments are designed to investigate hydro-mechanical processes controlling the enhancement of hydraulic properties in deep geothermal projects. We combine periodic pumping tests, high-resolution seismic monitoring, structural analysis and mine-back drilling into stimulated volumes in an effort to improve near-real-time monitoring, phenomenological models of the hydrofrac/hydroshear process, and prognosis strategies. The ongoing experiments are located at the Reiche Zeche underground laboratory in Freiberg, Saxony/Germany, at a depth of about 130 m below surface in strongly foliated metamorphic gneisses.The most recent field campaign and initial phase of STIMTEC-X in October 2020 involved eleven local stress measurements in three existing boreholes, previously used for monitoring purposes, with varying orientations and lengths. We hydraulically tested nine previously stimulated intervals and performed eight dilatometer tests in previously stimulated and new intervals to determine deformation characteristics of induced hydrofracs and pre-existing fractures. We monitored these operations in real-time using an adaptive, high-resolution seismic monitoring network comprising six acoustic emission (AE)-type hydrophones, six regular AE sensors and four accelerometers. Hydrophones were never installed before in combination with hydraulic gauges or the double packer probe used for localized injection as during STIMTEC-X. Hydrophones were optimally placed for each measurement configuration anew with at least one deployed in the direct vicinity (~3-4 m) of the injection interval to make best use of the existing infrastructure. This led to an improvement in detection and localisation of induced AE events. A series of active seismic measurements allowed us to establish the polarization, amplitude sensitivity, detection ranges, resonance frequencies and suitability to detect S-waves of the hydrophones. Good signal to noise ratios were recorded for distances up to 17 m. The range of incidence angles, including incidence angles from the opposite direction, in which the sensor is facing, was obtained that can be used for magnitude determination.A circulation experiment between the injection borehole and two newly drilled boreholes of 23 m and 30 m depth as part of STIMTEC-X is anticipated for March 2021. Here, we present lessons learned from seismic monitoring the STIMTEC and STIMTEC-X hydraulic stimulation campaigns and highlight the advantages of using adaptive and flexible networks. We present an overview of the STIMTEC-X experiment and first results addressing the heterogeneity in stress and deformational behaviour seen throughout the anisotropic reservoir.

Several powerful physics-based computational landslide run-out models have been developed and validated throughout the last years. The geohazards community applies these forward models in simulation tools to predict potential landslide run-out outcomes including their uncertainties, and uses inverse approaches to conduct reanalyses and to infer on model parameters for calibration purposes. Yet it remains challenging to turn these computational frameworks into robust, transparent and transferrable simulation-based decision support tools for geohazard mitigation. In particular, the landscape of uncertainties – such as those resulting from the idealised model description itself, input data (e.g., material parameters or topographic data), and numerical scheme related hyperparameters – is still not systematically managed when conducting landslide simulations. Probabilistic hazard maps that take these uncertainties into account imply a large number of model evaluations, which constitutes a computational bottle neck. This issue can be overcome by using High Performance Computing (HPC) resources along with the existing software and resources. Alternatively, physics-informed machine learning strategies use simulation results of the original process model, i.e., the simulator, to train a statistically valid representation, the so-called emulator. Once being trained, the emulator significantly reduces computational costs, while at the same time it grants access to an estimation of the introduced error. A software framework has recently been set up to integrate Gaussian process emulation and the landslide run-out model r.avaflow, an open-source mass flow simulation tool. Emulation-based sensitivity analysis was of comparable quality to conventional studies, and the computational costs were cut significantly. The emulator allowed for the first time to conduct a global sensitivity analyses at every location simultaneously for a complete landslide impact area. A joint effort across different institutes in Europe has been made in this contribution to test the potential and limitation of the emulation techniques by revisiting a number of published case studies. Selection of test cases has been made according to data availability, failure type and computational demand. Preliminary findings suggest that the emulator is capable of reducing the computational effort of modelling various flow-like landslides substantially. Future work will focus on curating a well-defined database of test scenarios across multiple institutes with cases ranging from small to medium-sized debris flows to large rock avalanches.

Between early 2018 and late 2019 the STIMTEC hydraulic stimulation experiment was performed at ca.~130 m below surface at the Reiche Zeche research mine in Freiberg, Saxony/Germany. The project aims at gaining insight into the creation and growth of fractures in anisotropic and heterogeneous crystalline rock units, to develop and optimise hydraulic stimulation techniques for EGS applications and to control the associated induced seismicity under in situ conditions. A series of ten hydro-frac experiments were performed in a 63 m-long, 15°-inclined injection borehole and five mini-fracs for stress measurements in a sub-vertical borehole. These were monitored using a seismic monitoring system of twelve high-sensitivity Acoustic emission (AE) sensors, three accelerometers and one broadband sensor. More than 11,000 high-frequency AE events with source sizes on the cm-to-dm scale accompanied the hydraulic stimulation in five of ten stimulated intervals in the injection borehole. Several hundred AE events were recorded during the mini-fracs in the vertical borehole. We investigate the characteristics of induced AE events by combining information obtained from high-accuracy event locations using a transversely isotropic P-wave velocity model per station with station corrections, relative hypocentre locations, and focal mechanism solutions of selected events. The AE event clouds extend ca. 5 m radially from the injection points and show variying orientations and dips. The ca. 150 focal mechanism solutions obtained using P-wave polarisations display mixed-mode failure with a significant portion of them showing compaction. The orientation of the maximum principal stress inferred from the hydro-fracs in the injection and vertical boreholes has a trend of N348°E and a plunge of 20°, as typical for southeast Germany. However, discrepancies in the magnitudes of the principal stresses were measured between these boreholes ca. 15 m apart, resulting in different faulting regimes. We present stress orientations obtained from inverting focal mechanism solutions to provide additional information for interpreting stress-characterisation measurements.

Measurements of snow avalanche impact pressures are performed at the Vallée de la Sionne test site since winter 1999. In these years of operation, we recorded the impact pressure of around 60 avalanches characterized by different flow regimes and dimensions.Pressure measurements were performed, simultaneously, on three different structures which are spatially distributed with a maximum distance of 30 m, in the run-out zone of the Vallée de la Sionne test site. The structure widths range from 0.25 to 1 m. On these structures pressure sensors ranging from small cells with 0.10 to 0.25 m in diameter to large pressure plates with area of 1m2 are mounted at different heights.A systematic analysis of all 60 avalanche data sets shows that the pressure measured at the different obstacles varies considerably, even within the same avalanche, both in space and time. Part of these differences can be attributed to different drag coefficients and dependence on obstacle size, but a large part of these differences can only be explained by the spatial variability of the flow properties and the temporal variability of the physical processes governing the interaction of the avalanche and the structures.In this contribution we show how spatial and temporal impact pressure variabilities correlate to avalanche dimension and flow regimes and we discuss the implication of such variations for structural design and hazard mapping.

Snow avalanches are natural disturbances that can cause substantial damage to forests, and endanger people and material assets. Knowledge of past avalanches is crucial for forest management and planning technical mitigation measures. Dendrogeomorphology can provide information on previous disturbances, for example tree damages, caused by avalanches in forested terrain. By analysing the past growth of trees, both temporal and spatial reconstructions of the avalanche activity in forests are possible.We use a dendrogeomorphological approach to study the past avalanche activity on an avalanche path above the city of Innsbruck in Austria. The area is of high importance for recreation (e.g. hiking, biking and skiing) as well as avalanche mitigation. Protection forest and technical protection measures are already in place (breaking mounds, catching and deflection dams) and frequently interact with avalanches. In January 2019, an avalanche with a destructive size of 3 - 4 released above the Arzler Alm mountain hut and caused considerable damage to approx. 25 ha of forest. This event provided us with the opportunity to conduct the present study. We sampled 104 trees along three longitudinal transects at elevation bands of 1200, 1100 and 1000 m a.s.l. covering the damaged area. We furthermore applied a selective sampling scheme below the forest damage along a gully where avalanches that reached the city of Innsbruck had previously been observed. Using an increment borer at least two cores per tree were taken from damaged and undisturbed trees. A mixture of conifers and broadleaved trees (mostly Picea abies (L.) Karst, Fagus sylvatica L. and Abies alba Mill.), as well as old and young trees was selected. In addition, we recorded the exact position of each tree and measured several tree parameters (e.g. diameter at breast height, tree height, damage description). Each core was then prepared following a standard dendrochronological procedure. Tree-rings were counted and ring-width was measured using a stereo microscope and a time-series analysis program (TSAP Win). Additionally, a visual detection of growth reactions (traumatic resin ducts, reaction wood, scars, callus tissue, growth suppression or releases) was performed, and tree-ring series were cross-dated and compared with local reference chronologies. Years with tree-rings showing growth anomalies potentially caused by ecological or climatic factors were discarded as possible avalanche years.Going forward we will compare years with major avalanche events identified by the dendrogeomorphological analysis, with existing extensive archival data and orthophotos. We expect this to confirm known events, but also to provide new information on unknown events. Based on the location of sampled trees, we will furthermore reconstruct the spatial extent of past events to estimate magnitude and frequency of avalanche activity in the area. Our results will also contribute to better predicting size and periodicity of future avalanche events and revealing potential changes in the avalanche regime. This in turn is relevant to calibrating and validating avalanche simulation models as well as for the design of technical and silvicultural protection and mitigation measures, which is especially important for an Alpine city like Innsbruck.

Recently, strong wind storms have caused large-scale damages in Alpine mountain forests, leaving the underlying infrastructure exposed. These forests often provide protection against gravitational natural hazard processes such as avalanches, rockfall and soil slides. To manage these disturbed forests efficiently and effectively, it is important to know 1) which forest areas serve a protective function to the underlying infrastructure, 2) what is the actual protective effect of these forests on the hazard process, and 3) how one could improve this effect.To define protective functions and to quantify the protective effects of forests, we created the Flow-Py model that identifies process areas of gravitational hazards, including avalanches, rockfall and debris slides. The model is written in Python to keep it easy adjustable. The run out routine of Flow-Py is based on the principles of energy conservation including frictional dissipation assuming simple coulomb friction, leading to constant travel-angle. Potential release areas and the corresponding travel angle have to be adapted for each type of gravitational mass movements. A important improvement, compared to similar models, is that it can handle mass movement in flat and uphill terrain. One major advantage of this model is its simplicity, resulting in a computationally inexpensive implementation, which allows for an application on a regional scale, covering large simulation areas. The adaptivity of the model further allows to consider existing infrastructure and to detect starting zones endangering the corresponding areas in a back-calculation step. Additionally, by adding forest cover to the simulations we can identify which forest area has a protective function and, based on information about forest structure, calculate the protective effect this forest provides to down slope infrastructure.Flow-Py is a useful tool to identify forest areas that are important for hazard protection (protective function) and to quantify their protective effect. The model can be applied in protection forest management to prioritize measures in wind throw areas. Furthermore, it is possible to use this tool for analyzing the protective functions and effects of different forest extents and structural conditions, for example, caused by climate change or forest disturbances. In this work we elaborate the potential of Flow-Py by presenting an avalanche case study in the central alpine region of Austria (Gries/Vals, Tyrol, AT). For this case the simulation results indicate a process area affected by avalanches of ~65% with respect to the total area of ~ 195 km².

Respiration pulses at rewetting are prominent features of soil responses to soil moisture fluctuations. These pulses are much larger compared to respiration rates under constant soil moisture, pointing to variations in water availability as drivers of the enhanced CO2 production. Moreover, the respiration pulses tend to be larger when soil moisture before rewetting is lower. Thus, both the pre-rainfall soil moisture and the variation in soil moisture control the size of the respiration pulse. While these patterns are known from empirical studies, models have struggled to capture the relations between rainfall statistical properties (frequency of occurrence and rain event depths) and the occurrence and size of respiration pulses, framing the scope of this contribution. Specifically, we ask – how are the statistical properties of respiration pulses related to rainfall statistics?Because rainfall can be regarded as a stochastic process generating variations in soil moisture, also respiration pulses at rewetting can be modelled through a probabilistic model. Here we develop such a model based on the premises that rainfall can be described as a marked Poisson process, and that respiration pulses increase with increasing variations of soil moisture (i.e., larger pulses after larger rain events) and decreasing pre-rain soil moisture (i.e., larger pulses after a long dry period). This model provides analytical relations between the statistical properties of soil respiration (e.g., long-term mean and standard deviation) and those of rainfall, allowing to study in a probabilistic framework how respiration varies along existing climatic gradients or in response to climatic changes that affect rainfall statistics.Results show that the long-term mean CO2 production during respiration pulses increases with increasing frequency and depth of rainfall events. However, the relative contribution of respiration pulses to the total microbial respiration decreases with rainfall frequency and depth. Similarly, also the variability of the size of respiration pulses, as measured by their standard deviation, decreases with increasing rainfall frequency and depth. As a consequence, climatic changes exacerbating rainfall intermittency – longer dry periods and more intense rain events – are predicted to increase both the relative contribution of respiration pulses to total microbial respiration and the variability of the pulse sizes.

Most of the current large scale hydrological models do not contain a physically-based groundwater flow component. The main difficulties in large-scale groundwater modeling include the efficient representation of unsaturated zone flow, the characterization of dynamic groundwater-surface water interaction and the numerical stability while preserving complex physical processes and high resolution. To address these problems, we propose a highly-scalable coupled hydrologic and groundwater model (mHM#OGS) based on the integration of two open-source modeling codes: the mesoscale hydrologic Model (mHM) and the finite element simulator OpenGeoSys (OGS). mHM#OGS is coupled using a boundary condition-based coupling scheme that dynamically links the surface and subsurface parts. Nested time stepping allows smaller time steps for typically faster surface runoff routing in mHM and larger time steps for slower subsurface flow in OGS. mHM#OGS features the coupling interface which can transfer the groundwater recharge and river baseflow rate between mHM and OpenGeoSys. Verification of the coupled model was conducted using the time-series of observed streamflow and groundwater levels. Moreover, we force the transient model using groundwater recharge in two scenarios: (1) spatially variable recharge based on the mHM simulations, and (2) spatially homogeneous groundwater recharge. The modeling result in first scenario has a slightly higher correlation with groundwater head time-series, which further validates the plausibility of spatial groundwater recharge distribution calculated by mHM in the mesocale. The statistical analysis of model predictions shows a promising prediction ability of the model. The offline coupling method implemented here can reproduce reasonable groundwater head time series while keep a desired level of detail in the subsurface model structure with little surplus in computational cost. Our exemplary calculations show that the coupled model mHM#OGS can be a valuable tool to assess the effects of variability in land surface heterogeneity, meteorological, topographical forces and geological zonation on the groundwater flow dynamics.

r.avaflow represents an innovative open source computational tool for routing rapid mass flows, avalanches or process chains from a defined release area down an arbitrary topography to a deposition area. In contrast to most existing computational tools, r.avaflow (i) employs a two-phase, interacting solid and fluid mixture model; (ii) is suitable for modelling more or less complex process chains and interactions; (iii) explicitly considers both entrainment and stopping i.e. the change of the basal topography; (iv) allows for the definition of multiple release masses and/or hydrographs; and (v) serves with built-in functionalities for validation, parameter optimization and sensitivity analysis. r.avaflow is freely available as a raster module of the GRASS GIS software, employing the programming languages Python and C along with the statistical software R. We exemplify the functionalities of r.avaflow by means of two sets of computational experiments: (1) generic process chains consisting in bulk mass and hydrograph release into a reservoir with entrainment of the dam and impact downstream; (2) the prehistoric Acheron rock avalanche, New Zealand. The simulation results are generally plausible for (1) and, after the optimization of two key parameters, reasonably in line with the corresponding observations for (2). However, we identify some potential to enhance the analytic and numerical concepts. Further, thorough parameter studies are necessary in order to make r.avaflow fit for reliable forward simulations of possible future mass flow events.

Numerous studies have been carried out on the community structure and diversity of biological soil crusts (BSCs) as well as their important functions on ecosystem processes. However, the amount of BSC-derived organic carbon (OC) input into soils and its chemical composition under natural conditions has rarely been investigated. In this study, different development stages of algae- and moss-dominated BSCs were investigated on a~natural (13C NMR spectroscopy and analysis of the carbohydrate-C signature.14C contents were used to assess the origin and dynamic of OC in BSCs and underlying substrates. Our results indicated a rapid BSC establishment and development from algae- to moss-dominated BSCs within only 4 yr under this temperate climate. The distribution of BSC types was presumably controlled by the surface stability according to the position in the slope. We found no evidence that soil properties influenced the BSC distribution on both sand dunes. 14C contents clearly indicated the existence of two OC pools in BSCs and substrates, recent BSC-derived OC and lignite-derived "old" OC (biologically refractory). The input of recent BSC-derived OC strongly decreased the mean residence time of total OC. The downward translocation of OC into the underlying substrates was only found for moss-dominated BSCs at the natural sand dune which may accelerate soil formation at these spots. BSC-derived OC mainly comprised O-alkyl C (carbohydrate-C) and to a lesser extent also alkyl C and N-alkyl C in varying compositions. Accumulation of alkyl C was only detected in BSCs at the experimental dune which may induce a~lower water solubility of BSC-derived extracellular polymeric substances when compared to BSCs at the natural sand dune indicating that hydrological effects of BSCs on soils depend on the chemical composition of the extracellular polymeric substances.

Floods and droughts are frequently causing large economic losses in China. These conditions vary in space, time, and magnitude. In this study, long-term meteorological and hydrological dryness and wetness conditions are analyzed for the Xijiang River Basin which is the largest tributary of the Zhujiang (Pearl) River. A very similar inter-annual course of precipitation and discharge can be observed. The standardized precipitation index (SPI) is used to show dryness and wetness pattern in the six sub-basins of the Xijiang River. The SPI-24 correlates high with the standardized discharge index (SDI-24) for Gaoyao hydrological station at the mouth of Xijiang River. Distinct long-term dryness and wetness sequences are found in the time series for the SPI-24 and SDI-24. The principal component analysis reveals many spatial interdependencies in dryness and wetness conditions for the sub-basins and explains some spatio-temporal disparities. Moderate dryness conditions have a larger spatial impact than moderate wetness conditions in the sub-basins. The loading pattern of the first principal component shows that the correlation with the entire Xijiang River Basin is highest in the eastern and lowest in the western sub-basins. Further spatial dipole conditions explain the spatio-temporal heterogeneity of dryness and wetness conditions. Accordingly, the precipitation in the eastern sub-basins contributes more to the hydrological wetness conditions than in the western sub-basins, which mainly contribute to dryness patterns. The spectral analysis for the SPI-24 (entire Xijiang River Basin) and SDI-24 shows similar peaks for periods of 11–14.7 yr, 2.8 yr, 3.4–3.7 yr, and 6.3–7.3 yr. The same periods can be found for the SPI-24 of Xijiang River's six sub-basins with some variability in the magnitude. The wavelet analysis shows that the most significant periods are stable over time since the 1980s. The extrapolations of the reconstructed time series do not suggest any spatial or temporal changes in the occurrence of dryness and wetness conditions in the next two decades but a continuation of the observed cycles at given magnitude. It can be concluded that long-term hydrological dryness and wetness conditions are directly caused by periodic cycles of meteorological conditions (i.e. precipitation). The applied methodologies prove to be able to identify spatial interdependencies and corresponding regional disparities, and to detect significant periodicities in long-term dryness and wetness conditions in the Xijiang River Basin.

We studied the relationships between crust microstructure, infiltration and water holding capacity under arid and temperate conditions (Factor A: Climate) on biological soil crusts (BSCs) sampled along a~catena on mobile sand dunes (Factor B: Catena). The arid study site was located near Nizzana, Israel (precipitation: 86 mm a−1, PET: ~2500 mm a−1) and the temperate site near Lieberose, Germany (precipitation: 569 mm a−1, PET: ~780 mm a−1). BSCs were sampled near the dune crest, at the centre of the dune slope and at the dune base at each site. Scanning electron microscopy (SEM) was used to characterize BSC morphology and microstructure. Infiltration was determined using microinfiltrometry under controlled moisture conditions in the lab. Water holding capacities were determined after water saturation of the dry BSCs. Wettability of the crusts was characterized using a "repellency index", which was calculated from water and ethanol sorptivities. Irrespective of the climate, an accumulation of fine particles in the BSCs was found, increasing along the catena from dune crest to dune base. Texture was finer and water holding capacities of the underlying substrate were higher at the arid site, whereas surface wettability was reduced at the temperate site. At both sites, BSCs caused extra water holding capacity compared to the substrate. Infiltration rates decreased along the catena and were generally lower at the dune slope and base of the arid site. A mechanism of crust stabilization is proposed where BSCs benefit from increased texture and biomass mediated water supply, and where the water supply to higher plants was limited due to alteration of physico-chemical surface properties under temperate conditions.

Dew formation was investigated in three developmental stages of biological soil crusts (BSC), which were collected along a catena of an inland dune and in the initial substrate. The Penman equation, which was developed for saturated surfaces, was modified for unsaturated surfaces and used for prediction of dewfall rates. The levels of surface saturation required for this approach were predicted using the water retention functions and the thicknesses of the BSCs. During a first field campaign (2–3 August 2011), dewfall increased from 0.042 kg m−2 for the initial sandy substrate to 0.058, 0.143 and 0.178 kg m−2 for crusts 1 to 3, respectively. During a second field campaign (17–18 August 2011), where dew formation was recorded in 1.5 to 2.75-h intervals after installation at 21:30 CEST, dewfall increased from 0.011 kg m−2 for the initial sandy substrate to 0.013, 0.028 and 0.055 kg m−2 for crusts 1 to 3, respectively. Dewfall rates remained on low levels for the substrate and for crust 1, and decreased overnight for crusts 2 and 3 (with crust 3 > crust 2 > crust 1 throughout the campaign). Dew formation was well reflected by the model response. The suggested mechanism of dew formation involves a delay in water saturation in near-surface soil pores and extracellular polymeric substances (EPS) where the crusts were thicker and where the water capacity was high, resulting in elevated vapor flux towards the surface. The results also indicate that the amount of dewfall was too low to saturate the BSCs and to observe water flow into deeper soil. Analysis of the soil water retention curves revealed that, despite the sandy mineral matrix, moist crusts clogged by swollen EPS pores exhibited a clay-like behavior. It is hypothesized that BSCs gain double benefit from suppressing their competitors by runoff generation and from improving their water supply by dew collection. Despite higher amounts of dew, the water availability to the crust community decreases with crust development, which may be compensated by ecophysiological adaptation of crust organisms, and which may further suppress higher vegetation or mosses.