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The goal of this article is to deepen our understanding of the entanglement of mathematics in the early COVID-19 discourse in Germany. A theoretical reflection and critique of the concept of mathematical literacy precedes and prepares an exploratory analysis of four concrete instances of expert-layperson communication in the form of media contributions. The theoretical reflection enriches Fischer's (1984, 2001, 2006, 2012) perspective on citizenship education by drawing connections between his work on the implicit and explicit culture of mathematics and other wellknown theories in critical mathematics education. These insights together with the analytical repertoire of Laclau & Mouffe's (2014) discourse theory provide tools and concepts which are used to describe different kinds of expert-layperson communication found in the data material. The empirical analysis details example cases in which mathematics plays a crucial part in the constitution of the roles of and relationships between experts and citizens resp. laypeople. We conclude that phenomena found in our material point to a general basic dilemma that is missing in explanations and suggestions on mathematics literacy in mathematics education discourse. [ABSTRACT FROM AUTHOR]

As the largest terrestrial carbon pool, soil organic carbon (SOC) has the potential to influence and mitigate climate change; thus, SOC monitoring is of high importance in the frameworks of various international treaties. Therefore, high-resolution SOC maps are required. Machine learning (ML) offers new opportunities to develop these maps due to its ability to data mine large datasets. The aim of this study was to apply three algorithms commonly used in digital soil mapping – random forest (RF), boosted regression trees (BRT), and support vector machine for regression (SVR) – on the first German agricultural soil inventory to model the agricultural topsoil (0–30 cm) SOC content and develop a two-model approach to address the high variability in SOC in German agricultural soils. Model performance is often limited by the size and quality of the soil dataset available for calibration and validation. Therefore, the impact of enlarging the training dataset was tested by including data from the European Land Use/Cover Area frame Survey for agricultural sites in Germany. Nested cross-validation was implemented for model evaluation and parameter tuning. Grid search and the differential evolution algorithm were also applied to ensure that each algorithm was appropriately tuned. The SOC content of the German agricultural soil inventory was highly variable, ranging from 4 to 480 g kg -1. However, only 4 % of all soils contained more than 87 g kg -1 SOC and were considered organic or degraded organic soils. The results showed that SVR produced the best performance, with a root-mean-square error (RMSE) of 32 g kg -1 when the algorithms were trained on the full dataset. However, the average RMSE of all algorithms decreased by 34 % when mineral and organic soils were modelled separately, with the best result from SVR presenting an RMSE of 21 g kg -1. The model performance was enhanced by up to 1 % for mineral soils and by up to 2 % for organic soils. Despite the ability of machine learning algorithms, in general, and SVR, in particular, to model SOC on a national scale, the study showed that the most important aspect for improving the model performance was to separate the modelling of mineral and organic soils. [ABSTRACT FROM AUTHOR]

The diffusion of renewable electricity technologies is widely considered as crucial for establishing a sustainable energy system in the future. However, the required transition is unlikely to be achieved by market forces alone. For this reason, many countries implement various policy instruments to support this process, also by re-distributing related costs among all electricity consumers. This paper presents a novel history-friendly agent-based study aiming to explore the efficiency of different mixes of policy instruments by means of a Differential Evolution algorithm. Special emphasis of the model is devoted to the possibility of small scale renewable electricity generation, but also to the storage of this electricity using small scale facilities being actively developed over the last decade. Both combined pose an important instrument for electricity consumers to achieve partial or full autarky from the electricity grid, particularly after accounting for decreasing costs and increasing efficiency of both due to continuous innovation. Among other things, we find that the historical policy mix of Germany introduced too strong and inflexible demand-side instruments (like feed-in tariff) too early, thereby creating strong path-dependency for future policy makers and reducing their ability to react to technological but also economic shocks without further increases of the budget. [ABSTRACT FROM AUTHOR]

• The HYPE model reproduces well the spatiotemporal variability of NO 3 - -N and TP. • Multi-site calibration increases the spatial capability of nutrient catchment model. • Hydro-meteorological sub-catchment delineation is important for nutrient prediction. Semi-distributed hydrological and water quality models are increasingly used as innovative and scientific-based management tools. However, their application is usually restricted to the gauging stations where they are originally calibrated, limiting their spatial capability. In this study, the semi-distributed hydrological water quality model HYPE (HYdrological Predictions for the Environment) was tested spatially to represent nitrate-N (NO 3 - - N) and total phosphorus (TP) concentrations and loads of the nested and heterogeneous Selke catchment (463 km2) in central Germany. First, an automatic calibration procedure and uncertainty analysis were conducted using the DiffeRential Evolution Adaptive Metropolis (DREAM) tool to simulate discharge, NO 3 - -N and TP concentrations. A multi-site and multi-objective calibration approach was applied using three main gauging stations, covering the most important hydro-meteorological and physiographical characteristics of the whole catchment. Second, the model's capability was tested to represent further internal stations, which were not initially considered for calibration. Results showed that discharge was well represented by the model at all three main stations during both calibration (1994–1998) and validation (1999–2014) periods with lowest Nash-Sutcliffe Efficiency (NSE) of 0.71 and maximum Percentage BIAS (PBIAS) of 18.0%. The model was able to reproduce the seasonal dynamics of NO 3 - -N and TP concentrations with low predictive uncertainty at the three main stations, reflected by PBIAS values in the ranges from −16.1% to 6.4% and from −20.0% to 11.5% for NO 3 - -N and TP load simulations, respectively. At internal stations, the model could represent reasonably well the seasonal variation of nutrient concentrations with PBIAS values in the ranges from −9.0% to 14.2% for NO 3 - -N and from −25.3% to 34.3% for TP concentration simulations. Overall, results suggested that the spatial validation of a nutrient transport model can be better ensured when a multi-site and multi-objective calibration approach using archetypical gauging stations is implemented. Further, results revealed that the delineation of sub-catchments should put more focus on hydro-meteorological conditions than on land-use features. [ABSTRACT FROM AUTHOR]

Nutrient catchment models are increasing considered as important decision-making tools in exploring the biogeochemical processes and testing the effect of future land use and climate changes effects. However, the development of a comprehensive decision tool requires an adequate evaluation of the model capability to predict the different physiographical characteristic of the studied catchment. For this purpose, the semi-distributed hydrological water quality model HYPE (Hydrological Predictions for the Environment) was tested to represent the measured Nitrate-N (NO3-N) and Total Phosphorus (TP) concentrations and loads at different spatially-distributed internal gauging stations that were not included in the calibration process. The nested meso-scale Selke catchment (463 km2, central Germany) was used for this study. First, discharge, NO3-N and TP concentrations were simulated at three gauging stations for a 20 year period in the main stem of the stream; Silberhuette, Meisdorf and Hausneindorf, representing the three key geographical features of the Selke catchment from upstream to downstream parts. Second, 13 internal stations, covering different stream order, soil type and land use classes were utilised for testing the spatiotemporal performance of the model. In this study, multi-site and multi-objective calibration approach and an uncertainty analysis were conducted using the DREAM (DiffeRential Evolution Adaptive Metropolis) algorithm. Results showed that the model could represent reasonably well the discharge for both calibration (1994-1998) and validation (1999-2014) periods at three main gauging stations, with lowest Nash-Sutcliffe efficiency (NSE) of 0.75 and percentage bias (PBIAS) of water balance of less than 18%. Results indicated less performance during the validation period, due to the reduction of number of precipitation stations compared to the calibration period. The dynamics of NO3-N and TP loads were well represented by the model in the three main gauging stations during the whole simulation period (1994-2014) with the lowest NSE and PBIAS of 0.71 and 9%, respectively. Results, also, showed that the HYPE model simulations agreed well with the measured NO3-N and TP loads for the whole simulation period in the 13 internal gauging stations. The model could mimic better the nutrient concentrations in the diffuse source-dominated sub-catchments compared to the point-source dominated sub-catchments. Results suggested that combination of multi-site and multi-objective calibration using key archetypes gauging stations can ensure sound spatiotemporal identification of the semi-distributed model parameters. [ABSTRACT FROM AUTHOR]