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Hydrological models can benefit from soft calibration, a process by which the proper simulation of hydrological variables is proved while or before addressing hard calibration. Soft calibration reduces the probability of obtaining a statistically accurate but unrealistic model. However, it requires soft data, which is often hard to acquire or unavailable. This work presents HydRoVars, an R tool developed to facilitate the estimation of data which can be implemented in a soft calibration procedure. It allows us to estimate two key hydrological indices (the runoff coefficient and baseflow index) and weather-related variables at the catchment scale for one or numerous basins. The runoff coefficient is calculated automatically from precipitation and streamflow datasets. Groundwater contribution is estimated through a semi-automatic process based on a baseflow filter which considers hydrogeological properties. Modellers would benefit from incorporating soft calibration in their calibration procedures, and this tool might help to estimate these relevant hydrological variables in their modelled area. The tool has been tested in 19 subbasins of the Tagus River basin (Spain) located in different geological regions. In the test cases, we demonstrate the usefulness of this tool to improve the model representation and gain an understanding of the catchments' hydrology. HIGHLIGHTS An R tool has been developed to collect weather and hydrological variables.; It allows estimating the runoff coefficient, and the baseflow index at the catchment scale.; Runoff coefficient can be automatically obtained for multiple basins.; Guidance is provided for a realistic groundwater contribution estimation.; Its usefulness has been tested in the Upper Tagus River basin (Spain).;

Abstract Input data collection, quality assurance and preparation are central but time_consuming steps in environmental modeling. Errors due to manual processing of model input data can result in an incorrect representation of an environmental system and may consequently lead to implausible model simulations. Correct input data preparation and thorough quality check at an early stage of the model setup procedure are essential to build confidence in model simulation results. Typically, in environmental model applications, many steps in the input data preparation phase have to be repeated with the inflow of new, additional or corrected data. In this study, we selected the widely used SWAT + ecohydrological model as an illustrative example to investigate challenges related to input data preparation. To assist in these tasks, we developed an R package named SWATprepR, which provides functions for typical and repeating SWAT + model input data preparation tasks. The package supports the preparation of weather input files, atmospheric deposition, soil parameters, crop rotations, and observed (control or calibration) data, to name a few, presently with focus on European applications. The SWATprepR functions are integrated in R script workflows and can help SWAT + modelers to avoid repetitive tasks, secure reproducibility and transparently document the data processing steps. Application of the package is illustrated with a test case of a SWAT + model for a small catchment in central Poland.

Abstract Freshwater ecosystems are considered biodiversity hotspots, but assessing the spatial distribution of species remains challenging. One major obstacle lies in the complex geospatial processing of large amounts of data, such as stream network, sub‐catchment and basin data, that are necessary for addressing the longitudinal connectivity among water bodies. Workflows thus need to be scalable, especially when working across large spatial extents and at high spatial resolution. This in turn requires advanced command‐line GIS skills and programming language integration, which often poses a challenge for freshwater researchers. To address this challenge, we developed the package hydrographr that provides scalable hydrographic data processing in R. The package contains functions for downloading data of the high‐resolution Hydrography90m dataset, processing, reading and extracting information, as well as assessing network distances and connectivity. While the functions are, by default, tailored toward the Hydrography90m data, they can also be generalised toward other data and purposes, such as efficient cropping and merging of raster and vector data, point‐raster extraction, raster reclassification and data aggregation. The package depends on the open‐source software GDAL/OGR, GRASS‐GIS and the AWK programming language in the Linux environment, allowing a seamless language integration. Since the data is processed outside R, hydrographr allows creating scalable geo‐processing workflows. We illustrate the hydrographr functions using two workflows that focus on (i) a freshwater species distribution modelling approach, and (ii) assessing stream connectivity given the fragmentation by dams. We also provide a detailed guide for the initial installation of the required software. Windows users need to first enable the Windows Subsystem for Linux (WSL) feature, and can then follow the same software installation as Linux users. hydrographr is maintained on GitHub at https://github.com/glowabio/hydrographr. hydrographr provides a set of key functions for processing freshwater geospatial data. We expect that the package will support the freshwater‐related research communities given the easy‐to‐use wrapper functions that allow capitalizing on powerful open‐source command‐line software, which may otherwise require a steep learning curve. Users can thus perform large‐scale freshwater‐specific longitudinal connectivity and network analyses across large geographic extents while staying within the R environment.

Study region: Catchment in Southern Italy. Study focus: Mediterranean waterways are commonly non-perennial; they are vulnerable to climate change (CC). Their management is particularly complex due to limited data availability. This work aims to develop a methodology for setting an Environmental Flow regime (E-Flows) for a temporary river (Locone, Italy) under limited data availability and under CC. As observed long-term time series of streamflow under natural conditions were not available, the Soil and Water Assessment Tool model (SWAT+) was applied to simulate the daily streamflow for the baseline period (1980–2010) and future (2020–2050) based on observed and model climate projections, respectively. A specific workflow was developed for model calibration focusing on the low flows. The hydrological regime was characterized by means of Indicators of Hydrological Alteration (IHAs), whereas the Range of Variability Approach (RVA) was applied to define the E-Flows. New hydrological insights for the region: The basin is experiencing a statistically significant increase in the air temperatures observed from 1971 to 2020, which is also predicted to continue in the future. Consequently, the average annual streamflow and monthly streamflow in winter and spring is expected to decrease. The calibration, based on a multi-objective model evaluation, improved the low-flow simulation. The detected differences in IHAs for the predicted periods should be considered in future water management when setting E-Flows for temporary rivers.

Abstract Extracellular vesicles (EVs) are highly interesting for the design of next-generation therapeutics. However, their preparation methods face challenges in standardization, yield, and reproducibility. Here, we describe a highly efficient and reproducible EV preparation method for monodisperse nano plasma membrane vesicles (nPMVs), which yields 10 to 100 times more particles per cell and hour than conventional EV preparation methods. nPMVs are produced by homogenizing giant plasma membrane vesicles following cell membrane blebbing and apoptotic body secretion induced by chemical stressors. nPMVs showed no significant differences compared to native EVs from the same cell line in cryo-TEM analysis, in vitro cellular interactions, and in vivo biodistribution studies in zebrafish larvae. Proteomics and lipidomics, on the other hand, suggested substantial differences consistent with the divergent origin of these two EV types and indicated that nPMVs primarily derive from apoptotic extracellular vesicles. nPMVs may provide an attractive source for developing EV-based pharmaceutical therapeutics.

Abstract Extracellular vesicle (EV) research increasingly demands for quantitative characterisation at the single vesicle level to address heterogeneity and complexity of EV subpopulations. Emerging, commercialised technologies for single EV analysis based on, for example, imaging flow cytometry or imaging after capture on chips generally require dedicated instrumentation and proprietary software not readily accessible to every lab. This limits their implementation for routine EV characterisation in the rapidly growing EV field. We and others have shown that single vesicles can be detected as light diffraction limited fluorescent spots using standard confocal and widefield fluorescence microscopes. Advancing this simple strategy into a process for routine EV quantitation, we developed ‘EVAnalyzer’, an ImageJ/Fiji (Fiji is just ImageJ) plugin for automated, quantitative single vesicle analysis from imaging data. Using EVAnalyzer, we established a robust protocol for capture, (immuno‐)labelling and fluorescent imaging of EVs. To exemplify the application scope, the process was optimised and systematically tested for (i) quantification of EV subpopulations, (ii) validation of EV labelling reagents, (iii) in situ determination of antibody specificity, sensitivity and species cross‐reactivity for EV markers and (iv) optimisation of genetic EV engineering. Additionally, we show that the process can be applied to synthetic nanoparticles, allowing to determine siRNA encapsulation efficiencies of lipid‐based nanoparticles (LNPs) and protein loading of SiO2 nanoparticles. EVAnalyzer further provides a pipeline for automated quantification of cell uptake at the single cell–single vesicle level, thereby enabling high content EV cell uptake assays and plate‐based screens. Notably, the entire procedure from sample preparation to the final data output is entirely based on standard reagents, materials, laboratory equipment and open access software. In summary, we show that EVAnalyzer enables rigorous characterisation of EVs with generally accessible tools. Since we further provide the plugin as open‐source code, we expect EVAnalyzer to not only be a resource of immediate impact, but an open innovation platform for the EV and nanoparticle research communities.

Thermal effusivity tester (TET) is a new device to measure the thermal conductivity and the thermal effusivity (heat dissipation) of textiles under a defined compression, developed at the R&D department of Lenzing AG (Austria). The device performance was tested by comparing its results with results from commercially available devices Alambeta, TCi Thermal Conductivity Analyzer and Kawabata KES-f thermal module. The fabrics tested were typical knit and weave constructions made of different fiber types, including cotton, wood-based cellulosics and polyester. For most of the fabrics, thermal effusivity results show wide agreement among TET, Alambeta and TCi, and strong positive correlation (r > 0.82) with heat flow (Qmax) as obtained from KES. Deviations were observed for some thicker and more resilient fabrics, most probably caused by the differences in the pressure applied by the devices on the fabric surface. The results show that TET offers a reliable and experimentally flexible approach to assessing thermal effusivity on textile structures and emphasizing the role of the dimensional change induced by the measurement conditions on the measured thermal effusivity and conductivity.

SWAT+ is a revised version of the SWAT model that has the capability to route flow across landscape units in the catchment, which is expected to improve the spatial representation of processes in watersheds. We applied the SWAT+ model in the Uruguay River Basin, an international river basin in South America with a total surface area of 370,000 km2, in order to (1) assess the water balance components, (2) represent their spatial distribution, and (3) examine their changes over time. The catchment was divided into uplands and floodplains and a decision table rule was developed based on streamflow data. The SPOTPY Python library was linked to SWAT+ and used as a tool to perform sensitivity analyses and calibration. The model represented the fluctuations of discharge well, although there was a general tendency to underestimate peak flows. Blue (precipitation and runoff) and green (evapotranspiration and soil water content) hydrological components were spatially plotted. Overall, SWAT+ simulated a realistic spatial distribution of the water cycle components. A seasonal Mann–Kendall test suggests a positive increasing trend in the average temperature (p-value = 0.007; Sen’s slope = 0.09), the soil water content (p-value = 0.02; Sen’s slope = 1.29), and evapotranspiration (p-value: 0.03; Sen’s slope = 1.97), indicating that the ecosystem experienced a changing climate during the simulation period. The findings presented in this study are of significant value for the impacts of sustainable management and the evaluation of climate change on water resources in the Uruguay River Basin.

Abstract Background Service model design is slowly being recognized among eHealth developers as a valuable method for creating durable implementation strategies. Nonetheless, practical guidelines and case-studies that inform the community on how to design a service model for an eHealth innovation are lacking. This study describes the development of a service model for an eHealth service, titled ‘SALSA’, which intends to support older adults with a physically active and socially inclusive lifestyle. Methods The service model for the SALSA service was developed in eight consecutive rounds, using a mixed-methods approach. First, a stakeholder salience analysis was conducted to identify the most relevant stakeholders. In rounds 2–4, in-depth insights about implementation barriers, facilitators and workflow processes of these stakeholders were gathered. Rounds 5 and 6 were set up to optimize the service model and receive feedback from stakeholders. In rounds 7 and 8, we focused on future implementation and integrating the service model with the technical components of the eHealth service. Results While the initial goal was to create one digital platform for the eHealth service, the results of the service modelling showed how the needs of two important stakeholders, physiotherapists and sports trainers, were too different for integrating them in one platform. Therefore, the decision was made to create two platforms, one for preventive (senior sports activities) and one for curative (physical rehabilitation) purposes. Conclusions A service model shows the interplay between service model design, technical development and business modelling. The process of service modelling helps to align the interests of the different stakeholders to create support for future implementation of an eHealth service. This study provides clear documentation on how to conduct service model design processes which can enable future learning and kickstart new research. Our results show the potential that service model design has for service development and innovation in health care.

Inheritance fundamentally violates the meritocratic justice principle of society. Despite the high level of wealth concentration and the fact that few people would be affected, political support for an inheritance tax is rather low. The topic of inheritance is not only about wealth but about values. We combine both by using questions in the Austrian Household Finance and Consumption Survey tailored to examine family values. The main aim of the paper is to bring sociological concepts and perceptions into the economic analysis of the role of inheritance in wealth distribution. We find several inconsistencies in people’s perceptions concerning the relation of inheritance to issues of social justice. We argue that family values are decisive for negative perceptions of inheritance taxation. Our empirical evidence suggests that in order to understand the resistance to inheritance taxation in society better, family values have to be taken into account. The main aim of the paper is to deliver empirical evidence for bringing an interdisciplinary approach, including sociological concepts, into economic analysis when analysing the relationship between inheritance, wealth and taxation.

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.

Environmental modeling studies aim to infer the impacts on environmental variables that are caused by natural and human-induced changes in environmental systems. Changes in environmental systems are typically implemented as discrete scenarios in environmental models to simulate environmental variables under changing conditions. The scenario development of a model input usually involves several data sources and perhaps other models, which are potential sources of uncertainty. The setup and the parametrization of the implemented environmental model are additional sources of uncertainty for the simulation of environmental variables. Yet to draw well-informed conclusions from the model simulations it is essential to identify the dominant sources of uncertainty. In impact studies in two Austrian catchments the eco-hydrological model Soil and Water Assessment Tool (SWAT) was applied to simulate discharge and nitrate-nitrogen (NO3--N) loads under future changing conditions. For both catchments the SWAT model was set up with different spatial aggregations. Non-unique model parameter sets were identified that adequately reproduced observations of discharge and NO3--N loads. We developed scenarios of future changes for land use, point source emissions, and climate and implemented the scenario realizations in the different SWAT model setups with different model parametrizations, which resulted in 7000 combinations of scenarios and model setups for both catchments. With all model combinations we simulated daily discharge and NO3--N loads at the catchment outlets. The analysis of the 7000 generated model combinations of both case studies had two main goals: (i) to identify the dominant controls on the simulation of discharge and NO3--N loads in the two case studies and (ii) to assess how the considered inputs control the simulation of discharge and NO3--N loads. To assess the impact of the input scenarios, the model setup, and the parametrization on the simulation of discharge and NO3--N loads, we employed methods of global sensitivity analysis (GSA). The uncertainties in the simulation of discharge and NO3--N loads that resulted from the 7000 SWAT model combinations were evaluated visually. We present approaches for the visualization of the simulation uncertainties that support the diagnosis of how the analyzed inputs affected the simulation of discharge and NO3--N loads. Based on the GSA we identified climate change and the model parametrization as being the most influential model inputs for the simulation of discharge and NO3--N loads in both case studies. In contrast, the impact of the model setup on the simulation of discharge and NO3--N loads was low, and the changes in land use and point source emissions were found to have the lowest impact on the simulated discharge and NO3--N loads. The visual analysis of the uncertainty bands illustrated that the deviations in precipitation of the different climate scenarios to historic records dominated the changes in simulation outputs, while the differences in air temperature showed no considerable impact.

The main objective of this study was to calibrate and validate the eco-hydrological model Soil and Water Assessment Tool (SWAT) with satellite-based actual evapotranspiration (AET) data from the Global Land Evaporation Amsterdam Model (GLEAM_v3.0a) and from the Moderate Resolution Imaging Spectroradiometer Global Evaporation (MOD16) for the Ogun River Basin (20 292 km2) located in southwestern Nigeria. Three potential evapotranspiration (PET) equations (Hargreaves, Priestley–Taylor and Penman–Monteith) were used for the SWAT simulation of AET. The reference simulations were the three AET variables simulated with SWAT before model calibration took place. The sequential uncertainty fitting technique (SUFI-2) was used for the SWAT model sensitivity analysis, calibration, validation and uncertainty analysis. The GLEAM_v3.0a and MOD16 products were subsequently used to calibrate the three SWAT-simulated AET variables, thereby obtaining six calibrations–validations at a monthly timescale. The model performance for the three SWAT model runs was evaluated for each of the 53 subbasins against the GLEAM_v3.0a and MOD16 products, which enabled the best model run with the highest-performing satellite-based AET product to be chosen. A verification of the simulated AET variable was carried out by (i) comparing the simulated AET of the calibrated model to GLEAM_v3.0b AET, which is a product that has different forcing data than the version of GLEAM used for the calibration, and (ii) assessing the long-term average annual and average monthly water balances at the outlet of the watershed. Overall, the SWAT model, composed of the Hargreaves PET equation and calibrated using the GLEAM_v3.0a data (GS1), performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool. The 95 % uncertainty of the SWAT-simulated variable bracketed most of the satellite-based AET data in each subbasin. A validation of the simulated soil moisture dynamics for GS1 was carried out using satellite-retrieved soil moisture data, which revealed good agreement. The SWAT model (GS1) also captured the seasonal variability of the water balance components at the outlet of the watershed. This study demonstrated the potential to use remotely sensed evapotranspiration data for hydrological model calibration and validation in a sparsely gauged large river basin with reasonable accuracy. The novelty of the study is the use of these freely available satellite-derived AET datasets to effectively calibrate and validate an eco-hydrological model for a data-scarce catchment.

Nitrous oxide (N2O) is a potent greenhouse gas stemming mainly from nitrogen (N)-fertilizer application. It is challenging to quantify N2O emissions from agroecosystems because of the dearth of measured data and high spatial variability of the emissions. The eco-hydrological model SWAT (Soil and Water Assessment Tool) simulates hydrological processes and N fluxes in a catchment. However, the routine for simulating N2O emissions is still missing in the SWAT model. A submodule was developed based on the outputs of the SWAT model to partition N2O from the simulated nitrification by applying a coefficient (K2) and also to isolate N2O from the simulated denitrification (N2O + N2) with a modified semi-empirical equation. The submodule was applied to quantify N2O emissions and N2O emission factors from selected crops in two agricultural catchments by using NH4NO3 fertilizer and the combination of organic N and NO3− fertilizer as N input data. The setup with the combination of organic N and NO3− fertilizer simulated lower N2O emissions than the setup with NH4NO3 fertilizer. When the water balance was simulated well (absolute percentage error 2O emissions was captured. More research to test the submodule with measured data is needed.