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Infections are often caused by pathobionts, endogenous bacteria that belong to the microbiota. Trauma and surgical intervention can allow bacteria to overcome host defences, ultimately leading to sepsis if left untreated. One of the main defence strategies of the immune system is the production of highly specific antibodies. In the present proof-of-concept study, plasma antibodies against 9 major pathogens were measured in sepsis patients, as an example of severe systemic infections. The binding of plasma antibodies to bacterial extracellular proteins was quantified using a semi-automated immunoblot assay. Comparison of the pathogen-specific antibody levels before and after infection showed an increase in plasma IgG in 20 out of 37 tested patients. This host-directed approach extended the results of pathogen-oriented microbiological and PCR diagnostics: a specific antibody response to additional bacteria was frequently observed, indicating unrecognised poly-microbial invasion. This might explain some cases of failed, seemingly targeted antibiotic treatment.

The trunks of elephants have prominent wrinkles from their base to the very tip. But neither the obvious differences in wrinkles between elephant species nor their development have been studied before. In this work, we characterize the lifelong development of trunk wrinkles in Asian and African elephants. Asian elephants have more dorsal major, meaning deep and wide, trunk wrinkles (approx. 126 ± 25 s.d.) than African elephants (approx. 83 ± 13 s.d.). Both species have more dorsal than ventral major trunk wrinkles and a closer wrinkle spacing distally than proximally. In Asian elephants, wrinkle density is high in the ‘trunk wrapping zone’. Wrinkle numbers on the left and right sides of the distal trunk differed as a function of trunk lateralization, with frequent bending in one direction causing wrinkle formation. Micro-computed tomography (microCT) imaging and microscopy of newborn elephants’ trunks revealed a constant thickness of the putative epidermis, whereas the putative dermis shrinks in the wrinkle troughs. During fetal development, wrinkle numbers double every 20 days in an early exponential phase. Later wrinkles are added slowly, but at a faster rate in Asian than African elephants. We discuss the relationship of species differences in trunk wrinkle distribution and number with behavioural, environmental and biomechanical factors.

This paper presents a methodological tool for dynamic reconstruction of the state of the ocean, based, as an example, on observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) experiment. The data used in this study were collected in the Amundsen Basin between October 2019 and January 2020. Analysing observational data to assess tracer field and upper-ocean dynamics is highly challenging when measurement platforms drift with the ice pack due to continuous drift speed and direction changes. We have equipped the new version of the coastal branch of the global Finite-volumE sea ice–Ocean Model (FESOM-C) with a nudging method. Model nudging was carried out assuming a quasi-steady state. Overall, the model can reproduce the lateral and vertical structure of the temperature, salinity, and density fields, which allows for projecting dynamically consistent features of these fields onto a regular grid. We identify two separate depth ranges of enhanced eddy kinetic energy located around two maxima in buoyancy frequency: the depth of the upper halocline and the depth of the warm (modified) Atlantic Water. Simulations reveal a notable decrease in surface layer salinity and density in the Amundsen Basin towards the north but no significant gradient from east to west. However, we find a mixed-layer deepening from east to west, with a 0.084 m km−1 gradient at 0.6 m km−1 standard deviation, compared to a weak deepening from south to north. The model resolves several stationary eddies in the warm Atlantic Water and provides insights into the associated dynamics. The model output can be used to further analyse the thermohaline structure and related dynamics associated with mesoscale and submesoscale processes in the central Arctic, such as estimates of heat fluxes or mass transport. The developed nudging method can be utilized to incorporate observational data from a diverse set of instruments and for further analysis of data from the MOSAiC expedition.

Background: There are no generally accepted criteria for selecting patients with recurrent glioblastoma for surgery. This retrospective study in a Danish population-based cohort aimed to identify prognostic factors affecting postoperative survival after repeated surgery for recurrent glioblastoma and to test if the preoperative New Scale for Recurrent Glioblastoma Surgery (NSGS) developed by Park CK et al could assist in the selection of patients for repeat glioblastoma surgery. Methods: Clinical data from 66 patients with recurrent glioblastoma and repeated surgery were analyzed. Kaplan–Meier plots were produced to illustrate survival in each of the three NSGS prognostic groups, and Cox proportional hazard regression was used to identify prognostic variables. Multivariable analysis was used to identify differences in survival in the three prognostic groups. Results: Six variables significantly affected postoperative survival: preoperative Karnofsky Performance Status (KPS)

A model of an amplified common rail fuel system is simulated in Matlab toanalyze the wave mechanics in the rail. The injectors are modeled as asystem of linear and non-linear ODE’s consisting of masses, a helical spring,compressibility effects from fluid volumes, and hydraulic flow throughorifices. The injector simulation then predicts the rate of oil consumption,which is then input into the rail model.The rail is modeled in three sections which are coupled together. The pointswhere the coupling occurs are the locations where the current firinginjector and the pump supply are connected to the rail. This allows themodel to control the pressure and velocity (as boundary conditions) atthese points. The rail model is based on the 1D, undamped wave equation,in a non-dimensional form [1] (in the position variable, x.) The Reduction ofOrder method was used to solve the wave equation with the Matlabfunction PDEPE.The model was run with two different sets of initial conditions - nominal(constant pressure and zero velocity,) and worst case using a simplifiedrepresentation of the pressure and velocity distribution at start of injection.This was done to determine the effect of rail waves at the start of injection,on the output of the model. The variation in fuel delivery, due to the variationin rail pressure, was then evaluated at three operating conditions - Idle,Peak Torque (PT) and High Speed Light Load (HSLL.) The simulation outputis then compared to analytical solutions of two forms of simplifiedgeometry, using the product method to solve the system [1.]

Abstract In recent decades, the Greenland ice sheet has been losing mass through glacier retreat and ice flow acceleration. This mass loss is linked with variations in submarine melt, yet existing ocean models are either coarse global simulations focused on decadal‐scale variability or fine‐scale simulations for process‐based investigations. Here, we unite these scales with a framework to downscale from a global state estimate (15 km) into a regional model (3 km) that resolves circulation on the continental shelf. We further downscale into a fjord‐scale model (500 m) that resolves circulation inside fjords and quantifies melt. We demonstrate this approach in Scoresby Sund, East Greenland, and find that interannual variations in submarine melt at Daugaard‐Jensen glacier induced by ocean temperature variability are consistent with the decadal changes in glacier ice dynamics. This study provides a framework by which coarse‐resolution models can be refined to quantify glacier submarine melt for future ice sheet projections.

Aquaculture is expected to play a vital role in solving the challenge of sustainably providing the growing world population with healthy and nutritious food. Pathogen outbreaks are a major risk for the sector, so early detection and a timely response are crucial. This can be enabled by monitoring the pathogen levels in aquaculture facilities. This paper describes a photonic biosensing platform based on silicon nitride waveguide technology with integrated active components, which could be used for such applications. Compared to the state of the art, the current system presents improvements in terms of miniaturization of the Photonic Integrated Circuit (PIC) and the development of wafer-level processes for hybrid integration of active components and for material-selective chemical and biological surface modification. Furthermore, scalable processes for integrating the PIC in a microfluidic cartridge were developed, as well as a prototype desktop readout instrument. Three bacterial aquaculture pathogens (Aeromonas salmonicida, Vagococcus salmoninarum, and Yersinia ruckeri) were selected for assay development. DNA biomarkers were identified, corresponding primer-probe sets designed, and qPCR assays developed. The biomarker for Aeromonas was also detected using the hybrid PIC platform. This is the first successful demonstration of biosensing on the hybrid PIC platform.

An AuDHD researcher recounts the highs and lows of relocating from the United States to Germany for his postdoc.

Animal Protection and Antisemitism in German Professional Press in the Years 1919–1939 The article discusses the entanglement of antisemitism and animal protection movement on the basis of the professional press of German veterinarians, animal protectionists, and meat producers from 1919 until 1939. The focal point is the question of shehitah. The article shows to what extent this question was a part of a general debate on the slaughter reform and the development of new stunning methods. Not surprisingly, most of the articles in the analyzed press were in favor of the introduction of the stunning obligation before the slaughter, which was from the Jewish perspective equal to the ban of shehitah. The article differentiates between the arguments of the majority of proponents of the obligation and those of their radical antisemitic colleagues. It argues that the animal protection movement in its majority cannot be described as racist or as a natural partner of national socialists, for only some representatives of it, such as Max Müller or Rudolf Einhauser from Munich, displayed a clear antisemitic rhetoric.

We live in a time of unprecedented scientific and human progress while being increasingly aware of its negative impacts on our planet’s health. Aerial, terrestrial, and aquatic ecosystems have significantly declined putting us on course to a sixth mass extinction event. Nonetheless, the advances made in science, engineering, and technology have given us the opportunity to reverse some of our ecosystem damage and preserve them through conservation efforts around the world. However, current conservation efforts are primarily human led with assistance from conventional robotic systems which limit their scope and effectiveness, along with negatively impacting the surroundings. In this perspective, we present the field of bioinspired robotics to develop versatile agents for future conservation efforts that can operate in the natural environment while minimizing the disturbance/impact to its inhabitants and the environment’s natural state. We provide an operational and environmental framework that should be considered while developing bioinspired robots for conservation. These considerations go beyond addressing the challenges of human-led conservation efforts and leverage the advancements in the field of materials, intelligence, and energy harvesting, to make bioinspired robots move and sense like animals. In doing so, it makes bioinspired robots an attractive, non-invasive, sustainable, and effective conservation tool for exploration, data collection, intervention, and maintenance tasks. Finally, we discuss the development of bioinspired robots in the context of collaboration, practicality, and applicability that would ensure their further development and widespread use to protect and preserve our natural world.

An extensive field campaign, the Ems-Dollard Measurements (EDoM), was executed in the Ems Estuary, bordering the Netherlands and Germany, aimed at better understanding the mechanisms that drive the exchange of water and sediments between a relatively exposed outer estuary and a hyper-turbid tidal river. More specifically, the reasons for the large up-estuary sediment accumulation rates and the role of the tidal river on the turbidity in the outer estuary were insufficiently understood. The campaign was designed to unravel the hydrodynamic and sedimentary exchange mechanisms, comprising two hydrographic surveys during contrasting environmental conditions using eight concurrently operating ships and 10 moorings measuring for at least one spring–neap tidal cycle. All survey locations were equipped with sensors measuring flow velocity, salinity, and turbidity (and with stationary ship surveys taking water samples), while some of the survey ships also measured turbulence and sediment settling properties. These observations have provided important new insights into horizontal sediment fluxes and density-driven exchange flows, both laterally and longitudinally. An integral analysis of these observations suggests that large-scale residual transport is surprisingly similar during periods of high and low discharge, with higher river discharge resulting in both higher seaward-directed fluxes near the surface and landward-directed fluxes near the bed. Sediment exchange seems to be strongly influenced by a previously undocumented lateral circulation cell driving residual transport. Vertical density-driven flows in the outer estuary are influenced by variations in river discharge, with a near-bed landward flow being most pronounced in the days following a period with elevated river discharge. The study site is more turbid during winter conditions, when the estuarine turbidity maximum (ETM) is pushed seaward by river flow, resulting in a more pronounced impact of suspended sediments on hydrodynamics. All data collected during the EDoM campaign, but also standard monitoring data (waves, water levels, discharge, turbidity, and salinity) collected by Dutch and German authorities are made publicly available at 4TU Centre for Research Data (https://doi.org/10.4121/c.6056564.v3; van Maren et al., 2022).

Automated feeders have long fed mice, livestock, and poultry, but are incapable of feeding zoo animals such as gorillas. In captivity, gorillas eat cut vegetables and fruits in pieces too large to be dispensed by automated feeders. Consequently, captive gorillas are fed manually at set times and locations, keeping them from the exercise and enrichment that accompanies natural foraging. We designed and built ForageFeeder, an automated gorilla feeder that spreads food at random intervals throughout the day. ForageFeeder is an open source and easy to manufacture and modify device, making the feeder more accessible for zoos. The design presented here reduces manual labor for zoo staff and may be a useful tool for studies of animal ethology.

Very large and comprehensive datasets are increasingly used in the field of hydrology. Large-sample studies provide insights into the hydrological cycle that might not be available with small-scale studies. LamaH-CE (LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, LamaH for short; the geographical extension “-CE” is omitted in the text and the dataset) is a new dataset for large-sample studies and comparative hydrology in Central Europe. It covers the entire upper Danube to the state border of Austria–Slovakia, as well as all other Austrian catchments including their foreign upstream areas. LamaH covers an area of about 170 000 km2 in nine countries, ranging from lowland regions characterized by a continental climate to high alpine zones dominated by snow and ice. Consequently, a wide diversity of properties is present in the individual catchments. We represent this variability in 859 gauged catchments with over 60 catchment attributes, covering topography, climatology, hydrology, land cover, vegetation, soil and geological properties. LamaH further contains a collection of runoff time series as well as meteorological time series. These time series are provided with a daily and hourly resolution. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with a high temporal resolution. The runoff time series are classified by over 20 attributes including information about human impacts and indicators for data quality and completeness. The structure of LamaH is based on the well-known CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets. In contrast, however, LamaH does not only consider independent basins, covering the full upstream area. Intermediate catchments are covered as well, which allows together with novel attributes the considering of the hydrological network and river topology in applications. We not only describe the basic datasets used and methodology of data preparation but also focus on possible limitations and uncertainties. LamaH contains additionally results of a conceptual hydrological baseline model for checking plausibility of the inputs as well as benchmarking. Potential applications of LamaH are outlined as well, since it is intended to serve as a uniform data basis for further research. LamaH is available at https://doi.org/10.5281/zenodo.4525244 (Klingler et al., 2021).

GFZ (German Research Centre for Geosciences) set up the Zugspitze Geodynamic Observatory Germany with a worldwide unique installation of a superconducting gravimeter at the summit of Mount Zugspitze on top of the Partnach spring catchment. This high alpine catchment is well instrumented, acts as natural lysimeter and has significant importance for water supply to its forelands, with a large mean annual precipitation of 2080 mm and a long seasonal snow cover period of 9 months, while showing a high sensitivity to climate change. However, regarding the majority of alpine regions worldwide, there is only limited knowledge on temporal water storage variations due to sparsely distributed hydrological and meteorological sensors and the large variability and complexity of signals in alpine terrain. This underlines the importance of well-equipped areas such as Mount Zugspitze serving as natural test laboratories for improved monitoring, understanding and prediction of alpine hydrological processes. The observatory superconducting gravimeter, OSG 052, supplements the existing sensor network as a novel hydrological sensor system for the direct observation of the integral gravity effect of total water storage variations in the alpine research catchment at Zugspitze. Besides the experimental set-up and the available data sets, the gravimetric methods and gravity residuals are presented based on the first 27 months of observations from 29 December 2018 to 31 March 2021. The snowpack is identified as being a primary contributor to seasonal water storage variations and, thus, to the gravity residuals with a signal range of up to 750 nm s−2 corresponding to 1957 mm snow water equivalent measured with a snow scale at an altitude of 2420 m at the end of May 2019. Hydro-gravimetric sensitivity analysis reveal a snow–gravimetric footprint of up to 4 km distance around the gravimeter, with a dominant gravity contribution from the snowpack in the Partnach spring catchment. This shows that the hydro-gravimetric approach delivers representative integral insights into the water balance of this high alpine site.

Experimental watersheds have a long tradition as research sites in hydrology and have been used since the late nineteenth and early twentieth centuries. The University of Natural Resources and Life Sciences Vienna (BOKU) recently extended its experimental research forest site “Rosalia” with an area of 950 ha towards the creation of a full ecological-hydrological experimental watershed. The overall objective is to implement a multi-scale, multi-disciplinary observation system that facilitates the study of water, energy and solute transport processes in the soil–plant–atmosphere continuum. This article describes the characteristics of the site and the monitoring network and its instrumentation that has been installed since 2015, as well as the datasets. The network includes four discharge gauging stations and seven rain gauges along with observations of air and water temperature, relative humidity, and electrical conductivity. In four profiles, soil water content and temperature are recorded at different depths. In addition, since 2018, nitrate, TOC and turbidity have been monitored at one gauging station. In 2019, a programme to collect isotopic data in precipitation and discharge was initiated. All data collected since 2015, including, in total, 56 high-resolution time series (with 10 min sampling intervals), are provided to the scientific community on a publicly accessible repository. The datasets are available at https://doi.org/10.5281/zenodo.3997140 (Fürst et al., 2020).

Abstract Submarine melting at Greenland's marine terminating glaciers is a crucial, yet poorly constrained process in the coupled ice‐ocean system. Application of Antarctic melt rate representations, derived for floating glacier tongues, to non‐floating marine terminating glaciers commonly found in Greenland, results in a dramatic underestimation of submarine melting. Here, we revisit the physical theory underlying melt rate parameterizations and leverage recently published observational data to derive a novel melt rate parameterization. This is the first parameterization that (a) consistently comprises both convective‐ and shear‐dominated melt regimes, (b) includes coefficients quantitatively constrained using observational data, and (c) is applicable to any vertical glacier front. We show that, compared to the current state‐of‐the‐art approach, the scheme provides an improved fit to observed melt rates on the scale of the terminating front, offering an opportunity to incorporate this critical missing forcing into ocean circulation models.

Field trips to museums and performing arts centers are not a frequent activity for most students, so how do cultural organizations learn to make the most of these limited but potentially valuable “gateway experiences” toward increased arts participation? This cross-sectional study examines the engagement of students during a performance at a performing arts center with data collected over three seasons of performances across art forms, grade levels, and student populations (students who attend Title I schools, English learners, and students with disabilities) under the umbrella of Positive Youth Development theory. The findings of regression analyses and analyses of variance feature three aspects that may optimize positive engagement in the arts experience for students: prior attendance to performances, lessons in the art form, and preparation prior to the performance. However, when the data is disaggregated by priority populations, not all the factors predict higher positive engagement in a statistically significant way. The feature that may have a consistent incidence across all populations, and exclusively for students with disabilities, is preparation prior to the performance. These results present practical implications for audience recruitment, accessibility supports, and learning design for diverse learners.

Urban areas struck by disasters such as earthquakes are in need of a fast damage detection assessment. A post-event SAR image often is the first available image, most likely with no matching pre-event image to perform change detection. In previous work we have introduced a debris detection algorithm for this scenario that is trained exclusively with synthetically generated training data. A classification step is employed to separate debris from similar textures such as vegetation. In order to verify the use of a random forest classifier for this context, we conduct a performance comparison with two alternative popular classifiers, a support vector machine and a convolutional neural network. With the direct comparison revealing the random forest classifier to be best suited, the effective performance on the prospect of debris detection is investigated for the post-earthquake Christchurch scene. Results show a good separation of debris from vegetation and gravel, thus reducing the false alarm rate in the damage detection operation considerably.

Persistent Scatterer Interferometry (PSInSAR) exploits a time series of Synthetic Aperture Radar (SAR) images to estimate the mean velocity with which the surface of the earth is deforming. However, most PSInSAR algorithms estimate the mean velocities using a linear regression model. Since some deformation phenomena can exhibit a more complex behavior over time, using a linear regression model leads to potentially wrong estimations for the mean velocity. For example, the velocity of a landslide moving down a steep slope can change depending on the water content of the material of the landslide, or an inactive landslide can reactivate due to an earthquake. Both scenarios would not result in a time series with a constant linear slope but in a piecewise linear time series.This paper presents a Matlab-based tool to analyze an individual Persistent Scatterer (PS) time series. The Persistent Scatterer Deformation Pattern Analysis Tool (PSDefoPAT) aims to build a mathematical model that sufficiently describes the time series trend and seasonal and noise components. The trend component is estimated using polynomial regression and piecewise linear models, while a sine function approximates the seasonal component. The goal is to identify the best fitting model for the displacement time series of a PS. PSDefoPAT is introduced by examine the time series of three different PS located in the region surrounding Patras, Greece. Based on the derived models, we discuss the nature of their deformation patterns.

For many ungauged mountain regions, global datasets of different meteorological and land surface parameters are the only data sources available. However, their applicability in modelling high-alpine regions has been insufficiently investigated so far. Therefore, we tested a suite of globally available datasets by applying the physically based Cold Regions Hydrological Model (CRHM) for a 10-year (September 2000–August 2010) period in the gauged high-alpine Research Catchment Zugspitze (RCZ), which is 12 km2 and located in the European Alps. Besides meteorological data, snow depth is measured at two stations. We ran CRHM with a reference run with in situ-measured meteorological data and a 2.5 m high-resolution digital elevation model (DEM) for the parameterization of the surface characteristics. Regarding different meteorological setups, we used 10 different globally available datasets (including versions of ERA, GLDAS, CFSR, CHIRPS) and additionally one transferred dataset from a similar station in the vicinity. Regarding the different DEMs, we used ALOS (Advanced Land Observing Satellite) and SRTM (Shuttle Radar Topography Mission) (both 30 m) as well as GTOPO30 (1 km). The following two main goals were investigated: (a) the reliability of simulations of snow depth, specific snow hydrological parameters and runoff with global meteorological products and (b) the influence of different global DEMs on snow hydrological simulations in such a topographically complex terrain. The range between all setups in mean decadal temperature is high at 3.5 ∘C and for the mean decadal precipitation sum at 1510 mm, which subsequently leads to large offsets in the snow hydrological results. Only three meteorological setups, the reference, the transferred in situ dataset and the CHIRPS dataset, substituting precipitation only, showed agreeable results when comparing modelled to measured snow depth. Nevertheless, those setups showed obvious differences in the catchment's runoff regime and in snow depth, snow cover, ablation period, the date, and quantity of maximum snow water equivalent in the entire catchment and in specific parts. All other globally available meteorological datasets performed worse. In contrast, all globally available DEM setups reproduced snow depth, the snow hydrological parameters and runoff quite well. Differences occurred mainly due to differences in radiation model input due to different spatial realizations. Even though SRTM and ALOS have the same spatial resolution, they showed considerable differences due to their different product origins. Despite the fact that the very coarse GTOPO30 DEM performed relatively well on the catchment mean, we advise against using this product in such heterogeneous high-alpine terrain since small-scale topographic characteristics cannot be captured. While global meteorological data are not suitable for sound snow hydrological modelling in the RCZ, the choice of the DEM with resolutions in the decametre level is less critical. Nevertheless, global meteorological data can be a valuable source to substitute single missing variables. For the future, however, we expect an increasing role of global data in modelling ungauged high-alpine basins due to further product improvements, spatial refinements and further steps regarding assimilation with remote sensing data.

Water temperature in rivers is a crucial environmental factor with the ability to alter hydro-ecological as well as socio-economic conditions within a catchment. The development of modelling concepts for predicting river water temperature is and will be essential for effective integrated water management and the development of adaptation strategies to future global changes (e.g. climate change). This study tests the performance of six different machine-learning models: step-wise linear regression, random forest, eXtreme Gradient Boosting (XGBoost), feed-forward neural networks (FNNs), and two types of recurrent neural networks (RNNs). All models are applied using different data inputs for daily water temperature prediction in 10 Austrian catchments ranging from 200 to 96 000 km2 and exhibiting a wide range of physiographic characteristics. The evaluated input data sets include combinations of daily means of air temperature, runoff, precipitation and global radiation. Bayesian optimization is applied to optimize the hyperparameters of all applied machine-learning models. To make the results comparable to previous studies, two widely used benchmark models are applied additionally: linear regression and air2stream. With a mean root mean squared error (RMSE) of 0.55 ∘C, the tested models could significantly improve water temperature prediction compared to linear regression (1.55 ∘C) and air2stream (0.98 ∘C). In general, the results show a very similar performance of the tested machine-learning models, with a median RMSE difference of 0.08 ∘C between the models. From the six tested machine-learning models both FNNs and XGBoost performed best in 4 of the 10 catchments. RNNs are the best-performing models in the largest catchment, indicating that RNNs mainly perform well when processes with long-term dependencies are important. Furthermore, a wide range of performance was observed for different hyperparameter sets for the tested models, showing the importance of hyperparameter optimization. Especially the FNN model results showed an extremely large RMSE standard deviation of 1.60 ∘C due to the chosen hyperparameters. This study evaluates different sets of input variables, machine-learning models and training characteristics for daily stream water temperature prediction, acting as a basis for future development of regional multi-catchment water temperature prediction models. All preprocessing steps and models are implemented in the open-source R package wateRtemp to provide easy access to these modelling approaches and facilitate further research.

The Universal Soil Loss Equation (USLE) is the most commonly used model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and steepness LS, soil cover C, and support measures P. A large variety of methods exist to derive these model inputs from readily available data. However, the estimated values of a respective model input can strongly differ when employing different methods and can eventually introduce large uncertainties in the estimated soil loss. The potential to evaluate soil loss estimates at a large scale is very limited due to scarce in-field observations and their comparability to long-term soil estimates. In this work we addressed (i) the uncertainties in the soil loss estimates that can potentially be introduced by different representations of the USLE input factors and (ii) challenges that can arise in the evaluation of uncertain soil loss estimates with observed data. In a systematic analysis we developed different representations of USLE inputs for the study domain of Kenya and Uganda. All combinations of the generated USLE inputs resulted in 972 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on the administrative level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation. The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude, particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large-scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively, while LS was relevant in small-scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.

Several advanced DInSAR techniques have been used to map surface deformations due to volcanism, active tectonics, landslides, subsidence, and uplift as well as to monitor the deformation of critical infrastructure such as bridges and dams. Recently, studies have explored the potential of these techniques to be integrated into a permanently operating monitoring system. ESA’s Sentinel-1 satellites have been providing SAR images for such a purpose since 2014. Nowadays, it is easy to access more than 230 SAR images of any area of interest, and update this dataset every six days with a new image.Due to the high frequency of the data acquisition, the question arises on how to best handle such a dataset. Is it suitable to always consider the whole available dataset or would a partial processing of the dataset and combining the results at a later point be more appropriate? To answer these questions, three different processing strategies are investigated in this paper. The first is a continuously growing dataset and for the second and third strategy, the dataset was divided into sub-stacks with and without overlap. In this study, the key parameters of each strategy are analyzed. In addition, the size of the sub-stacks is varied and the results are compared.

The unit hydrograph (UH) has been one of the most widely employed hydrological modelling techniques to predict rainfall–runoff behaviour of hydrological catchments, and is still used to this day. Its concept is based on the idea that a unit of effective precipitation per time unit (e.g. mm h−1) will always lead to a specific catchment response in runoff. Given its relevance, the UH is an important topic that is addressed in most (engineering) hydrology courses at all academic levels. While the principles of the UH seem to be simple and easy to understand, teaching experiences in the past suggest strong difficulties in students' perception of the UH theory and application. In order to facilitate a deeper understanding of the theory and application of the UH for students, we developed a simple and cheap lecture theatre experiment which involved active student participation. The seating of the students in the lecture theatre represented the hydrological catchment in its size and form. A set of plastic balls, prepared with a piece of magnetic strip to be tacked to any white/black board, each represented a unit amount of effective precipitation. The balls are evenly distributed over the lecture theatre and routed by some given rules down the catchment to the catchment outlet, where the resulting hydrograph is monitored and illustrated at the black/white board. The experiment allowed an illustration of the underlying principles of the UH, including stationarity, linearity, and superposition of the generated runoff and subsequent routing. In addition, some variations of the experimental setup extended the UH concept to demonstrate the impact of elevation, different runoff regimes, and non-uniform precipitation events on the resulting hydrograph. In summary, our own experience in the classroom, a first set of student exams, as well as student feedback and formal evaluation suggest that the integration of such an experiment deepened the learning experience by active participation. The experiment also initialized a more experienced based discussion of the theory and assumptions behind the UH. Finally, the experiment was a welcome break within a 3 h lecture setting, and great fun to prepare and run.

Terrestrial photography is a cost-effective and easy-to-use method for measuring and monitoring spatially distributed land surface variables. It can be used to continuously investigate remote and often inaccessible terrain. We focus on the observation of snow cover patterns in high mountainous areas. The high temporal and spatial resolution of the photographs have various applications, for example validating spatially distributed snow hydrological models. However, the analysis of a photograph requires a preceding georectification of the digital camera image. To accelerate and simplify the analysis, we have developed the "Photo Rectification And ClassificaTIon SoftwarE" (PRACTISE) that is available as a Matlab code. The routine requires a digital camera image, the camera location and its orientation, as well as a digital elevation model (DEM) as input. If the viewing orientation and position of the camera are not precisely known, an optional optimisation routine using ground control points (GCPs) helps to identify the missing parameters. PRACTISE also calculates a viewshed using the DEM and the camera position. The visible DEM pixels are utilised to georeference the photograph which is subsequently classified. The resulting georeferenced and classified image can be directly compared to other georeferenced data and can be used within any geoinformation system. The Matlab routine was tested using observations of the north-eastern slope of the Schneefernerkopf, Zugspitze, Germany. The results obtained show that PRACTISE is a fast and user-friendly tool, able to derive the microscale variability of snow cover extent in high alpine terrain, but can also easily be adapted to other land surface applications.