A method to extract the monomer friction coefficient from the linear viscoelastic behavior of linear, entangled polymer melts

The rheological properties of isotactic and atactic polypropylene melts are analyzed in order to obtain the equilibration time, τ e, and monomer friction coefficient, ζ. A procedure is proposed to determine τ e from the zero shear rate viscosity, η 0, using the magnitude of the molecular weight per entanglement, M e, from the literature. This procedure can be applied to both mono- and polydisperse linear, entangled polymer melts. For different polymers this procedure gives very similar results compared to the description of the storage and loss modulus of nearly monodisperse linear, entangled polymer melts by molecular based theories, as well as with values of ζ reported in the literature for linear, non-entangled polymer melts. It is observed that for isotactic and atactic polypropylene M e differs by a factor 1.25 depending on the approach taken. As a consequence, the magnitude of τ e and ζ differ by a factor of about 3.0 and 1.8, respectively. The knowledge of τ e (or ζ) is of importance in order to obtain a better understanding of flow induced crystallization experiments on iP

Towards a rheological classification of flow induced crystallization experiments of polymer melts

Departing from molecular based rheology and rubber theory, four different flow regimes are identified associated to (1) the equilibrium configuration of the chains, (2) orientation of the contour path, (3) stretching of the contour path, and (4) rotational isomerization and a deviation from the Gaussian configuration of the polymer chain under strong stretching conditions. The influence of the ordering of the polymer chains on the enhanced point nucleation, from which spherulites grow, and on fibrous nucleation, from which the shish-kebab structure develops, is discussed in terms of kinetic and thermodynamic processes. The transitions between the different flow regimes, and the associated physical processes governing the flow induced crystallization process, are defined by Deborah numbers based on the reptation and stretching time of the chain, respectively, as well as a critical chain stretch. An evaluation of flow induced crystallization experiments reported in the literature performed in shear, uniaxial and planar elongational flows quantitatively illustrates that the transition from an enhanced nucleation rate of spherulites towards the development of the shish-kebab structure correlates with the transition from the orientation of the chain segments to the rotational isomerization of the high molecular weight chains in the melt. For one particular case this correlation is quantified by coupling the wide angle X-ray diffraction and birefringence measurements of the crystallization process to numerical simulations of the chain stretch of the high molecular weight chains using the extended Pom-Pom model in a cross-slot flo

How well can people observe the flow state of temporary streams?

Even though more than half of the global river network does not have continuous flow, temporary (i.e., non-perennial) streams are poorly represented in traditional monitoring networks. Therefore, new approaches are needed to monitor these streams. Citizen science provides an interesting opportunity as people, equipped with smartphones, can observe the flow state of temporary streams. Such observations can go beyond a simple classification of flow vs. no flow and include ecologically important states, such as standing water, isolated pools, or wet streambeds. However, the quality of citizen science data for temporary streams has so far not been thoroughly assessed. Therefore, we asked more than 1,200 people during 23 field days to visually determine the flow state of eight temporary streams based on six classes ranging from a dry streambed to flowing water. Participants could most clearly distinguish a flowing stream from a non-flowing stream. The overall agreement between participants was 66%; 83% of the selected flow states were within one class of the most frequently selected flow state. The agreement with the expert was lower (56% chose the same class, and 79% chose a state within one class). Inconsistencies between the selected flow state and answers to specific yes-no statements about the temporary stream were largest for the dry streambed and damp/wet streambed states. These discrepancies were partly caused by participants looking at different parts of the stream (i.e., participants considered the flow state for a location further upstream or downstream). To ensure that all participants determine the flow state comparably, we recommend clear definitions of the flow state classes, detailed information on the exact location for which the flow state needs to be determined, as well as more training

Self-guided smartphone excursions in university teaching—experiences from exploring “Water in the City”

Like many other university teachers, we were faced with an unprecedented situation in spring 2020, when we had to cancel on-site teaching and excursions due to the Covid-19 pandemic. However, we were in the fortunate position that we had already started to develop a smartphone-based self-guided excursion on the topic of “Water in the City”. We accelerated this development and used it to replace the traditional group excursion in our Bachelor level introductory course in Hydrology and Climatology. The excursion of this course is visited by around 150 students each year. Because the student feedback was overall very positive, we used the self-guided excursion again in 2021 and plan to continue to use it in the coming years. In this paper, we describe the excursion, discuss the experiences of the students and ourselves, and present recommendations and ideas that could be useful for similar excursions at other universities

Citizen science approaches for water quality measurements

Citizen science has become a widely used approach in water quality studies. Although there are literature reviews about citizen science and water quality assessments, an overview of the most commonly used methods and their strengths and weaknesses is still lacking. Therefore, we reviewed the scientific literature on citizen science for surface water quality assessments and examined the methods and strategies used by the 72 studies that fulfilled our search criteria. Special attention was given to the parameters monitored, the monitoring tools, and the spatial and temporal resolution of the data collected in these studies. In addition, we discuss the advantages and disadvantages of the different approaches used in water quality assessments and their potential to complement traditional hydrological monitoring and research

The CrowdWater game: A playful way to improve the accuracy of crowdsourced water level class data

Data quality control is important for any data collection program, especially in citizen science projects, where it is more likely that errors occur due to the human factor. Ideally, data quality control in citizen science projects is also crowdsourced so that it can handle large amounts of data. Here we present the CrowdWater game as a gamified method to check crowdsourced water level class data that are submitted by citizen scientists through the CrowdWater app. The app uses a virtual staff gauge approach, which means that a digital scale is added to the first picture taken at a site and this scale is used for water level class observations at different times. In the game, participants classify water levels based on the comparison of the new picture with the picture containing the virtual staff gauge. By March 2019, 153 people had played the CrowdWater game and 841 pictures were classified. The average water level for the game votes for the classified pictures was compared to the water level class submitted through the app to determine whether the game can improve the quality of the data submitted through the app. For about 70% of the classified pictures, the water level class was the same for the CrowdWater app and game. For a quarter of the classified pictures, there was disagreement between the value submitted through the app and the average game vote. Expert judgement suggests that for three quarters of these cases, the game based average value was correct. The initial results indicate that the CrowdWater game helps to identify erroneous water level class observations from the CrowdWater app and provides a useful approach for crowdsourced data quality control. This study thus demonstrates the potential of gamified approaches for data quality control in citizen science projects

Shallow-groundwater-level time series and a groundwater chemistry survey from a boreal headwater catchment, Krycklan, Sweden

Shallow groundwater can respond quickly to precipitation and is the main contributor to streamflow in most catchments in humid, temperate climates. Therefore, it is important to have high-spatiotemporal-resolution data on groundwater levels and groundwater chemistry to test spatially distributed hydrological models. However, currently, there are few datasets on groundwater levels with a high spatiotemporal resolution because of the large effort required to collect these data. To better understand shallow groundwater dynamics in a boreal headwater catchment, we installed a network of groundwater wells in two areas in the Krycklan catchment in northern Sweden for a small headwater catchment (3.5 ha; 54 wells) and a hillslope (1 ha; 21 wells). The average well depth was 274 cm (range of 70-581 cm). We recorded the groundwater-level variation at 10-30 min intervals between 18 July 2018-1 November 2020. Manual water-level measurements (0-26 per well) during the summers of 2018 and 2019 were used to confirm and re-calibrate the automatic water-level measurements. The groundwater-level data for each well was carefully processed using six data quality labels. The absolute and relative positions of the wells were measured with a high-precision GPS and terrestrial laser scanner to determine differences in absolute groundwater levels and calculate groundwater gradients. During the summer of 2019, all wells with sufficient water were sampled once and analyzed for electrical conductivity, pH, absorbance, and anion and cation concentrations, as well as the stable isotopes of hydrogen and oxygen. The data are available at https://doi.org/10.5880/fidgeo.2022.020 (Erdbrugger et al., 2022). This combined hydrometric and hydrochemical dataset can be useful for testing models that simulate groundwater dynamics and evaluating metrics that describe subsurface hydrological connectivity

Accuracy of crowdsourced streamflow and stream level class estimates

Streamflow data are important for river management and the calibration of hydrological models. However, such data are only available for gauged catchments. Citizen science offers an alternative data source, and can be used to estimate streamflow at ungauged sites. We evaluated the accuracy of crowdsourced streamflow estimates for 10 streams in Switzerland by asking citizens to estimate streamflow either directly, or based on the estimated width, depth and velocity of the stream. Additionally, we asked them to estimate the stream level class by comparing the current stream level with a picture that included a virtual staff gauge. To compare the different estimates, the stream level class estimates were converted into streamflow. The results indicate that stream level classes were estimated more accurately than streamflow, and more accurately represented high and low flow conditions. Based on this result, we suggest that citizen science projects focus on stream level class estimates instead of streamflow estimates

Value of crowd‐based water level class observations for hydrological model calibration

While hydrological models generally rely on continuous streamflow data for calibration, previous studies have shown that a few measurements can be sufficient to constrain model parameters. Other studies have shown that continuous water level or water level class (WL‐class) data can be informative for model calibration. In this study, we combined these approaches and explored the potential value of a limited number of WL‐class observations for calibration of a bucket‐type runoff model (HBV) for four catchments in Switzerland. We generated synthetic data to represent citizen science data and examined the effects of the temporal resolution of the observations, the numbers of WL‐classes, and the magnitude of the errors in the WL‐class data on the model validation performance. Our results indicate that on average one observation per week for a one‐year period can significantly improve model performance compared to the situation without any streamflow data. Furthermore, the validation performance for model parameters calibrated with WL‐class observations was similar to the performance of the calibration with precise water level measurements. The number of WL‐classes did not influence the validation performance noticeably when at least four WL‐classes were used. The impact of typical errors for citizen‐science‐based estimates of WL‐classes on the model performance was small. These results are encouraging for citizen science projects where citizens observe water levels for otherwise ungauged streams using virtual or physical staff gauges