Excessive nutrient levels remain a significant barrier to community recovery in large rivers globally. Here we ask if nutrient reduction can lead to restoration of riverine communities through investigating key anthropogenic drivers of total nitrogen (TN) and total phosphorus (TP) concentrations that affect riverine benthic macroinvertebrate and diatom communities. We seek to identify potential benefits of stricter nutrient reductions under the European Water Framework Directive (WFD). Nutrient impacts on riverine communities were analyzed across Germany using Structural Equation Modelling to distinguish cascading nutrient contributions from land and in-stream sources, and Bayesian Integrated Nested Laplace Approximation to account for spatial dependency. Across 781 benthic macroinvertebrate and 200 diatom sampling sites, covering 40 % of river catchments in Germany, in-stream nutrient concentrations explained nutrient impacts on riverine communities better than land use proxies. Agriculture contributes the majority of nutrient input loads, but urban areas, despite their small extent (<10 % of land), generate 41 % of TP inputs. Elevated TP had the strongest impact among nutrients on riverine communities. Reductions in nutrient concentrations by up to 50 % can improve highly degraded catchments, but stricter reductions suggest limited benefits for moderate to good quality ecosystems, where other stressors may constrain recovery. Our findings underline the conservation value of land use transformation to reduce in-stream nutrients and the need for stricter regulation of pollutant mixtures to address the complex challenges facing riverine ecosystems.

Urbanization is a key factor driving water quality and biological communities in freshwater systems. While urbanization impacts on water resources were well-studied in urban areas, few studies have examined the growing impacts in non-urban areas. Here we investigate the spatiotemporal trends of urbanization using high-resolution impervious surface data of both urban and non-urban areas across Germany, and evaluate the ur-banization impacts on changes of run-off, nutrient emissions, and macroinvertebrate communities. The emission changes were modelled using the MONERIS-PCRaster tool, while the macroinvertebrate community responses to urbanization were determined using Generalized Least Square models. We find that urbanization expanded by 3.2% from 2006 to 2015 nationwide, and primarily occurred in less-populated regions. The non-urban areas contributed 19.3-19.6% of the imperviousness expansion nationwide, which were equivalent to the 'unac-counted' increases of 15.5-15.9% of run-off, 10.6-11.0% of total nitrogen, and 12.5-12.9% of total phosphorus when only emissions from urban areas were modelled. Incorporating the emissions from both urban and non -urban areas revealed significant impacts of urbanization on macroinvertebrate community composition changes, with increases in more pollution-resistant and non-native species (particularly in large rivers), and reductions of more sensitive taxa. The community responses were jointly determined by local catchment char-acteristics and urbanization stressors. Our results suggest neglected effects of non-urban impervious surfaces and the importance of using impervious surface data instead of urban land classes to better represent urbanization processes and impacts in long-term planning and management of freshwater systems.

Growing population and urbanization challenge water resources sustainability and require stringent solutions in terms of emission measurements and pollution controls. Advancements in observation techniques have improved the availability of impervious surface data that cover both urban and non-urban areas to assess the impacts of urbanization. However, most models used in macroscale studies continue to derive surface imperviousness based on land-use classes and population data, and the contributions of non-urban impervious surfaces to runoff and nutrient emissions remain largely ignored. Effects of different impervious surface data on the predicted runoff and nutrient emissions is investigated in this study for macroscale urban and non-urban areas in tandem by means of an extended urban module MONERIS -PCRaster to enable scenarios with high-resolution impervi-ousness data. The results showed that approximately 70% of the total runoff and nutrient emissions nationwide originated from low-to-medium populated impervious surfaces rather than from major urban catchments. Using high-resolution imperviousness data at various aggregation levels resulted in lower biased outputs of predicted runoff and nutrient emissions when compared to results using the estimated impervious data from land-use and population information. The impervious surface shares between urban and non-urban lands revealed the opposite trends of urbanization developments in the less populated areas versus an increasing contribution of emissions from non-urban areas rather than urban centers in densely populated municipalities. Overall, the non-urban impervious surface areas contributed 5-20% of the "hidden " runoff volumes and nutrient emissions from all impervious areas. The results of this study highlight the need of model adaptations regarding the increased availability of high-resolution imperviousness data and the trend of urbanization development beyond urban areas for more accurate quantification of potential flood risks and emission hotspots of macroscale urbanized areas for sustainable water resources management.

Floodplains remove nitrate from rivers through denitrification and thus improve water quality. The Danube River Basin (DRB) has been affected by elevated nitrate concentrations and a massive loss of intact floodplains and the ecosystem services they provide. Restoration measures intend to secure and improve these valuable ecosystem services, including nitrate removal. Our study provides the first large-scale estimate of the function of large active floodplains in the DRB to remove riverine nitrate and assesses the contribution of reconnection measures. We applied a nutrient emission model in 6 river systems and coupled it with denitrification and flooding models which we adapted to floodplains. The floodplains have the capacity to eliminate about 33,200 t nitrate-N annually, which corresponds to 6.5 % of the total nitrogen emissions in the DRB. More nitrate is removed in-stream at regular flow conditions than in floodplain soils during floods. However, increasing frequently inundated floodplain areas reveals greater potential for improvement than increasing the channel network. In total, we estimate that 14.5 % more nitrate can be removed in reconnected floodplains. The largest share of nitrogen emissions is retained in the Yantra and Tisza floodplains, where reconnections are expected to have the greatest impact on water quality. In absolute numbers, the floodplains of the lower Danube convert the greatest quantities of nitrate, driven by the high input loads. These estimates are subject to uncertainties due to the heterogeneity of the available input data. Still, our results are within the range of similar studies. Reconnections of large floodplains in the DRB can, thus, make a distinct contribution to improving water quality. A better representation of the spatial configuration of water quality functions and the effect of floodplain reconnections may support the strategic planning of such to achieve multiple benefits and environmental targets.

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

The European Water Framework Directive demands to assess and report the chemical and ecological status of water bodies (WB). Linking their status to drivers and pressures and deriving suitable mitigation measures require knowledge of the shape and area of WB catchments. We derived a network of 26 570 WB catchments in Germany using the hydrologically-defined drainage basins of the German federal states. We established a network of 338 149 drainage basins. This network underwent plausibility checks and a validation with the catchment areas of 348 monitoring stations across Germany. To this network, we assigned the longest intersecting or the next downstream WB code. To account for geometric inaccuracies we revised spurious intersections resulting in splittings and cycles in the WB network. As WB may be ecologically but not hydrologically well defined, we split them at confluences and intersections. The network of drainage basins matched the monitoring stations with a Nash-Sutcliffe efficiency of 1.00. The final WB network contained 11 005 out of the 11 586 original WBs longer than 1 m. The corresponding local catchment areas range from <<0.0001 to 446 km(2), with a median of 10 km(2). The dataset combines the requirements of hydrological and ecological modelling applications at basin or national scales with the needs of the EU reporting which can foster their acceptance by state authorities and river-basin management.

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named ‘Global Applications of Soil Erosion Modelling Tracker (GASEMT)’, includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.

Climate change is expected to affect the occurrence of heavy rainfall. We analyzed trends of heavy rainfall days for the last decades in Germany. For all available stations with daily data, days exceeding daily thresholds (10, 20, 30 mm) were counted annually. The Mann–Kendall trend test was applied to overlapping periods of 30 years (1951–2019). This period was extended to 1901 for 111 stations. The stations were aggregated by natural regions to assess regional patterns. Impacts of data inconsistencies on the calculated trends were evaluated with the metadata and recent hourly data. Although the trend variability depended on the chosen exceedance threshold, a general long-term trend for the whole of Germany was consistently not evident. After 1951, stable positive trends occurred in the mountainous south and partly in the northern coastal region, while parts of Central Germany experienced negative trends. The frequent location shifts and the recent change in the time interval for daily rainfall could affect individual trends but were statistically insignificant for regional analyses. A case study supported that heavy rains became more erosive during the last 20 years. The results showed the merit of historical data for a better understanding of recent changes in heavy rainfall.

Bayesian networks (BN) have increasingly been applied in water management but not to estimate the efficacy of riparian buffer zones (RBZ). Our methodical study aims at evaluating the first BN to predict the RBZ efficacy to retain sediment and nutrients (dissolved, total, and particulate nitrogen and phosphorus) from widely available variables (width, vegetation, slope, soil texture, flow pathway, nutrient form). To evaluate the influence of parent nodes and how the number of states affects prediction errors, we used a predefined general BN structure, collected 580 published datasets from North America and Europe, and performed classification tree analyses and multiple 10-fold cross-validations of different BNs. These errors ranged from 0.31 (two output states) to 0.66 (five states). The outcome remained unchanged without the least influential nodes (flow pathway, vegetation). Lower errors were achieved when parent nodes had more than two states. The number of efficacy states influenced most strongly the prediction error as its lowest and highest states were better predicted than intermediate states. While the derived BNs could support or replace simple design guidelines, they are limited for more detailed predictions. More representative data on vegetation or additional nodes like preferential flow will probably improve the predictive power.

The universal soil loss equation (USLE) is widely used to identify areas of erosion risk at regional scales. In Brandenburg, USLE R factors are usually estimated from summer rainfall, based on a relationship from the 1990s. We compared estimated and calculated factors of 22 stations with 10-min rainfall data. To obtain more realistic estimations, we regressed the latter to three rainfall indices (total and heavy-rainfall sums). These models were applied to estimate future R factors of 188 climate stations. To assess uncertainties, we derived eight scenarios from 15 climate models and two representative concentration pathways (RCP), and compared the effects of index choice to the choices of climate model, RCP, and bias correction. The existing regression model underestimated the calculated R factors by 40%. Moreover, using heavy-rainfall sums instead of total sums explained the variability of current R factors better, increased their future changes, and reduced the model uncertainty. The impact of index choice on future R factors was similar to the other choices. Despite all uncertainties, the results indicate that average R factors will remain above past values. Instead, the extent of arable land experiencing excessive soil loss might double until the mid-century with RCP 8.5 and unchanged land management.

Freshwater species are adapted to and depend on various discharge conditions, such as 32 indicators of hydrologic alteration (IHA). Knowing how these indicators will be altered under climate change is essential for predicting species response and to develop mitigation concepts. The simulation of IHA under climate change is subject to considerable uncertainties which should be considered to obtain credible and robust predictions. Therefore, we investigated the major uncertainties inherent in climate change data and processing: general circulation model (GCM) and regional climate model (RCM) choice, representative concentration pathway (RCP) scenario, bias correction (BC) method, all within three mesoscale catchments in the European ecoregions: Central Plains, Central Highlands, and Alpine. Highest uncertainties were caused by the GCM and RCM choice, followed by the type of BC and the RCP. For the prediction, we reduced these uncertainties tailored to the ideal depiction of the IHA in each ecoregion. Together with a significance test, this enabled a robust depiction of the change in IHA for two future time periods. We found diverging changes within the ecoregions, caused by the complex interaction between precipitation, temperature and the governing catchment hydrological processes. The results provide an important basis for further impact research, especially for ecological freshwater studies.

Sediment delivery ratios (SDRs) link soil loss to sediment yields (SYs) of river catchments. At large scales, SDR models rely on simple catchment properties. To assess their sensitivity and uncertainty in European regions, I compiled a sediment database and derived 16 soil loss maps varying the approximation of the factors of the universal soil loss equation (USLE). Additionally to parameterizing the USLE, the sensitivity analysis comprised the choice of the soil loss and SDR models. The USLE maps were compared to a Pan-European Soil Erosion Risk Assessment (PESERA) map and two empirical SDR models were applied for explaining the variability of SDR and SY. Most relevant for the model efficiency was the choice of the soil loss model. Unlike the PESERA map, the USLE with the four-parameter SDR model allowed satisfactory results in all regions. Fewer parameters for estimating SDR were less appropriate. Among the USLE factors, the choice of the K factor was most important. The overall model sensitivity to choosing the USLE map and the SDR model was similar. Based on the sensitivity analysis, a most suitable USLE map for predicting SYof European river catchments is proposed. The uncertainty in USLE estimates resulted in an uncertainty of modelled SY varying from 30% to 60% in different regions. No model realization grasped the variability of SDR and SY, so only regional SDR models were feasible. The model application and evaluation were hampered by unrepresentative sediment data and the inherent limitations of the modelling framework.

Pulsing storms and prolonged rainfall events have been associated to floods, soil erosion and nutrient fluxes in many European river catchments. This motivated us to develop a parsimonious approach to model the climate forcing on sediment yields in a mountainous Austrian–German river catchment. The hydro-climatologic forcing was interpreted by the novel RAMSES (RAinfall Model for SEdiment yield Simulation) approach to estimate the annual sediment yields. We used annual data on suspended-solid yields at the gauge Füssen, monitored from 1924 to 2003, and monthly rainfall data. The dataset was split into the period 1924–1969 for calibration and the period 1970–2003 for validation. The quality of sediment yield data was critically examined, and a few outlying years were identified and removed from further analyses. These outliers revealed that our model underestimates exceptionally high sediment yields in years of severe flood events. For all other years, the RAMSES performed well against the calibration set, with a correlation coefficient (r) equal to 0.83 and a Nash–Sutcliffe Index (NSI) of 0.69. The lower performance in the validation period (r=0.61, NSI=0.36) has to be partly attributed to discontinuities in the monitoring strategy. For the calibration dataset, monthly precipitations proved nonetheless to be better predictors for annual sediment yields than annual values. These first results lay the foundation for reconstructing intra- to inter-decadal variability of sediment yields in river catchments where detailed precipitation records are not available as well as for the reconstruction of historical sediment yields.