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Antimicrobial resistance (AMR) is a public health issue attributed to the misuse of antibiotics in human and veterinary medicine. Since AMR surveillance requires a One Health approach, we sampled nine interconnected compartments at a hydrological open-air lab (HOAL) in Austria to obtain six bacterial species included in the WHO priority list of antibiotic-resistant bacteria (ARB). Whole genome sequencing-based typing included core genome multilocus sequence typing (cgMLST). Genetic and phenotypic characterization of AMR was performed for all isolates. Eighty-nine clinically-relevant bacteria were obtained from eight compartments including 49 E. coli, 27 E. faecalis, 7 K. pneumoniae and 6 E. faecium. Clusters of isolates from the same species obtained in different sample collection dates were detected. Of the isolates, 29.2% were resistant to at least one antimicrobial. E. coli and E. faecalis isolates from different compartments had acquired antimicrobial resistance genes (ARGs) associated with veterinary drugs such as aminoglycosides and tetracyclines, some of which were carried in conjugative and mobilizable plasmids. Three multidrug resistant (MDR) E. coli isolates were found in samples from field drainage and wastewater. Early detection of ARGs and ARB in natural and farm-related environments can identify hotspots of AMR and help prevent its emergence and dissemination along the food/feed chain.

Various large-scale risk maps show that the eastern part of Austria, in particular the Pannonian Basin, is one of the regions in Europe most vulnerable to wind erosion. However, comprehensive assessments of the severity and the extent of wind erosion risk are still lacking for this region. This study aimed to prove the results of large-scale maps by developing high-resolution maps of wind erosion risk for the target area. For this, we applied a qualitative soil erosion assessment (DIN 19706) with lower data requirements and a more data-demanding revised wind erosion equation (RWEQ) within a GIS application to evaluate the process of assessing wind erosion risk. Both models defined similar risk areas, although the assignment of severity classes differed. Most agricultural fields in the study area were classified as not at risk to wind erosion (DIN 19706), whereas the mean annual soil loss rate modeled by RWEQ was 3.7 t ha−1 yr−1. August was the month with the highest modeled soil loss (average of 0.49 t ha−1 month−1), due to a low percentage of vegetation cover and a relatively high weather factor combining wind speed and soil moisture effects. Based on the results, DIN 19706 is suitable for a general classification of wind erosion-prone areas, while RWEQ can derive additional information such as seasonal distribution and soil loss rates besides the spatial extents of wind erosion.

The quantitative prediction of hydrological components through hydrological models could serve as a basis for developing better land and water management policies. This study provides a comprehensive step by step modelling approach for a small agricultural watershed using the SWAT model. The watershed is situated in Petzenkirchen in the western part of Lower Austria and has total area of 66 hectares. At present, 87% of the catchment area is arable land, 5% is used as pasture, 6% is forested and 2% is paved. The calibration approach involves a sequential calibration of the model starting from surface runoff, and groundwater flow, followed by crop yields and then soil moisture, and finally total streamflow and sediment yields. Calibration and validation are carried out using the r-package SWATplusR. The impact of each calibration step on sediment yields and total streamflow is evaluated. The results of this approach are compared with those of the conventional model calibration approach, where all the parameters governing various hydrological processes are calibrated simultaneously. Results showed that the model was capable of successfully predicting surface runoff, groundwater flow, soil profile water content, total streamflow and sediment yields with Nash-Sutcliffe efficiency (NSE) of greater than 0.75. Crop yields were also well simulated with a percent bias (PBIAS) ranging from −17% to 14%. Surface runoff calibration had the highest impact on streamflow output, improving NSE from 0.39 to 0.77. The step-wise calibration approach performed better for streamflow prediction than the simultaneous calibration approach. The results of this study show that the step-wise calibration approach is more accurate, and provides a better representation of different hydrological components and processes than the simultaneous calibration approach.

Knowledge of the evaporation (E) and transpiration (T) components of evapotranspiration (ET) is important for ecohydrological modeling and agricultural productivity. The stable-isotope method offers the possibility to partition E and T due to the distinct differences in the isotopic signals of the sources. In this study, the concentration and isotopic ratios for oxygen-18 (18O) of water vapor in the ecosystem boundary layer of a growing maize field at the Hydrological Open Air Laboratory (HOAL) catchment in Austria were measured using a high-frequency field-sampling device. In conjunction with isotope samples from the soil and maize plants, these data were used to partition ET using the Keeling plot technique. Eddy covariance and sap flow measurements were used to provide a comparison to test the stable-isotope method. The fraction of transpiration (Ft) calculated with the stable-isotope method showed good agreement with the sap flow method. Overall daily average values of Ft were in a range from 43.0 to 88.5% with T accounting for an average value of 67.5% of the evapotranspiration over the nine days of the experimental period. Following a precipitation event of 9.7 mm, Ft increased from 63.4 to 88.5% over the next four days as the upper layer of the soil dried out while the plants accessed deeper soil water.

The interaction between rainfall erosivity parameters and splash erosion is crucial for describing the soil erosion process; however, it is rarely investigated under natural rainfall conditions. In this study, we conducted splash erosion experiments under natural rainfall on three sites in Central Europe. The main goal was to obtain the relationship between splash erosion of the bare soil in seedbed condition and commonly used rainfall erosivity parameters (kinetic energy, intensity, and rainfall erosivity (EI30)). All sites were equipped with a rain gauge and an optical laser disdrometer where the splash erosion was measured, with modified Morgan splash cups. In order to investigate which parameter best describes the splash erosion process for all sites, a regression analysis was performed. In total, 80 splash erosion events were evaluated. Splash erosion can be described as a linear function of total kinetic energy and a non-linear function of EI30. However, the use of the total kinetic energy led to underestimation of the splash erosion rates for highly intensive rainfalls. Therefore, better results were obtained when using average rainfall intensity as the splash erosion predictor or the kinetic energy divided by the rainfall duration. Minor differences between the replicates during splash erosion measurements indicate that the modified Morgan splash cup provides a good tool for soil erosion assessment.

Besides the importance of dissolved organic matter (DOM) in soil biogeochemical processes, there is still a debate on how agricultural intensification affects the leaching of terrestrial DOM into adjacent aquatic ecosystems. In order to close this linkage, we conducted a short-term (45 day) lysimeter experiment with silt loam and sandy loam undisturbed/intact soil cores. Mineral (calcium ammonium nitrate) or organic (pig slurry) fertilizer was applied on the soil surface with a concentration equivalent to 130 kg N ha−1. On average, amounts of leached DOC over 45 days ranged between 20.4 mg (silt loam, mineral fertilizer) and 34.4 mg (sandy loam, organic fertilizer). Both, mineral and organic fertilization of a silt loam reduced concentration of dissolved organic carbon (DOC) in the leachate and shifted its composition towards a microbial-like signature (BIX) with a higher aromaticity (Fi) and a lower molecular size (E2:E3). However, in sandy loam only mineral fertilization affected organic matter leaching. There, lowered DOC concentrations with a smaller molecular size (E2:E3) could be detected. The overall effect of fertilization on DOC leaching and DOM composition was interrelated with soil texture and limited to first 12 days. Our results highlight the need for management measures, which prevent or reduce fast flow paths leading soil water directly into aquatic systems, such as surface flow, fast subsurface flow, or drainage water.

In the European Union, soil erosion is identified as one of the main environmental threats, addressed with a variety of rules and regulations for soil and water conservation. The by far most often officially used tool to determine soil erosion is the Universal Soil Loss Equation (USLE) and its regional adaptions. The aim of this study is to use three different regional USLE-based approaches in three different test catchments in the Czech Republic, Germany, and Austria to determine differences in model results and compare these with the revised USLE-base European soil erosion map. The different regional model adaptations and implementation techniques result in substantial differences in test catchment specific mean erosion (up to 75% difference). Much more pronounced differences were modelled for individual fields. The comparison of the region-specific USLE approaches with the revised USLE-base European erosion map underlines the problems and limitations of harmonization procedures. The EU map limits the range of modelled erosion and overall shows a substantially lower mean erosion compared to all region-specific approaches. In general, the results indicate that even if many EU countries use USLE technology as basis for soil conservation planning, a truly consistent method does not exist, and more efforts are needed to homogenize the different methods without losing the USLE-specific knowledge developed in the different regions over the last decades.

Soil erosion through various water processes is a worldwide problem. This research is focused on raindrops’ impact on soil surfaces, which is generally considered the initial stage of the erosion process. Splash erosion monitoring was conducted across three experimental sites: Petzenkirchen, Mistelbach (Austria) and Prague (Czech Republic). At each site, the rainfall characteristics (intensity and kinetic energy) were measured by rain gauges and disdrometers, and the impacts on soils (soil loss, soil surface consolidation, changes in soil surface roughness) were evaluated. Several disturbed soil samples with an area of 78.5 cm2 were placed into splash cups prior to each event. The splash cup collects the soil particles that are splashed out of the sample area when a raindrop hits the soil surface. The collected sediment suspension is processed in the laboratory after each event to determine the lost soil mass. Ground photogrammetry was utilized to determine the surface consolidation of a sample caused by a given rainfall event. Results for more than 500 soil samples were included in this study. Relationships between kinetic energy, rainfall intensity and soil loss and consolidation were evaluated.

Soil erosion by water is affected by the rainfall erosivity, which controls the initial detachment and mobilization of soil particles. Rainfall erosivity is expressed through the rainfall intensity (I) and the rainfall kinetic energy (KE). KE–I relationships are an important tool for rainfall erosivity estimation, when direct measurement of KE is not possible. However, the rainfall erosivity estimation varies depending on the chosen KE–I relationship, as the development of KE–I relationships is affected by the measurement method, geographical rainfall patterns and data handling. This study investigated how the development of KE–I relationships and rainfall erosivity estimation is affected by the use of different disdrometer types. Rainfall data were collected in 1-min intervals from six optical disdrometers at three measurement sites in Austria, one site in Czech Republic and one site in New Zealand. The disdrometers included two disdrometers of each of the following types: the PWS100 Present Weather Sensor from Campbell Scientific, the Laser Precipitation Monitor from Thies Clima and the first generation Parsivel from OTT Hydromet. The fit of KE–I relationships from the literature varied among disdrometers and sites. Drop size and velocity distributions and developed KE–I relationships were device-specific and showed similarities for disdrometers of the same type across measurement sites. This hindered direct comparison of results from different types of disdrometers, even when placed at the same site. Thus, to discern spatial differences in rainfall characteristics the same type of measurement instrument should be used.

Previous validation studies have demonstrated the accuracy of the Metop-A ASCAT soil moisture (SM) product, although over- and underestimation during different seasons of the year suggest a need for improving the retrieval algorithm. In this study, we analyzed whether adapting the vegetation characterization based on global parameters to regional conditions improves the seasonal representation of SM and vegetation optical depth ( τ ). SM and τ are retrieved from ASCAT using both a seasonal (mean climatological) and a dynamic vegetation characterization that allows for year-to-year changes. The retrieved SM and τ are compared with in situ and satellite SM, and with vegetation products (SMAP, AMSR2, and SPOT-VGT/PROBA-V). The study region is set in an agricultural area of Lower Austria that is characterized by heterogeneous land cover and topography, and features an experimental catchment equipped with a SM network (HOAL SoilNet). We found that a stronger vegetation correction within the SM retrieval improves the SM product considerably (increase of the Spearman correlation coefficient r s by 0.15 on average, and r s comparable to SMAP and AMSR2). The vegetation product derived with a dynamic vegetation characterization compares well to the reference datasets and reflects vegetation dynamics such as start and peak of season and harvest. Although some vegetation effects cannot be corrected by the adapted vegetation characterization, our results demonstrate the benefits of a parameterization optimized for regional conditions in this temperate climate zone.

Crop monitoring is of great importance for e.g., yield prediction and increasing water use efficiency. The Copernicus Sentinel-1 mission operated by the European Space Agency provides the opportunity to monitor Earth’s surface using radar at high spatial and temporal resolution. Sentinel-1’s Synthetic Aperture Radar provides co- and cross-polarized backscatter, enabling the calculation of microwave indices. In this study, we assess the potential of Sentinel-1 VV and VH backscatter and their ratio VH/VV, the cross ratio (CR), to monitor crop conditions. A quantitative assessment is provided based on in situ reference data of vegetation variables for different crops under varying meteorological conditions. Vegetation Water Content (VWC), biomass, Leaf Area Index (LAI) and height are measured in situ for oilseed-rape, corn and winter cereals at different fields during two growing seasons. To quantify the sensitivity of backscatter and microwave indices to vegetation dynamics, linear and exponential models and machine learning methods have been applied to the Sentinel-1 data and in situ measurements. Using an exponential model, the CR can account for 87% and 63% of the variability in VWC for corn and winter cereals. In oilseed-rape, the coefficient of determination ( R 2 ) is lower ( R 2 = 0.34) due to the large difference in VWC between the two growing seasons and changes in vegetation structure that affect backscatter. Findings from the Random Forest analysis, which uses backscatter, microwave indices and soil moisture as input variables, show that CR is by and large the most important variable to estimate VWC. This study demonstrates, based on a quantitative analysis, the large potential of microwave indices for vegetation monitoring of VWC and phenology.