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Soil loss by water erosion is one of the main threats to soil health and food production in intensively used agricultural areas. To assess its significance to overall sediment production, we applied the Water and Tillage Erosion Model/Sediment Delivery model (WaTEM/SEDEM) to the Luoyugou catchment, a subcatchment of the Yellow River Basin within the Chinese Loess Plateau. WaTEM/SEDEM considers rill and interrill erosion and deposition rates to calculate the sediment yield rates leaving the catchment. Terraces were established in the 1990s to reduce soil loss in this area, but no soil erosion modeling has been published regarding the effect of this mitigation measure. Therefore, we applied 1000 Monte Carlo simulations of the WaTEM/SEDEM, and the modeled average soil loss by rill and interrill erosion for 2020 was 12.2 ± 0.5 t ha−1 yr−1, with a sediment yield at the outlet of 53,207.8 ± 11,244.1 t yr−1. The results indicated that the terracing reduced gross soil loss rates (from 51.8 t ha−1 yr−1 in 1986 to 12.2 ± 0.5 t ha−1 yr−1 in 2020), while land cover changes, mainly the conversion of forests and grassland, partly counteracted the mitigation (combined effect: 76% reduction). Modeled sediment loads by rill and interrill erosion accounted for 22.8% of the total long-term sediment production recorded by flow discharge measurements. Other processes not considered by the model, such as landslides, gully erosion, riverbank erosion, and sediment production by construction, seem to predominantly influence the overall sediment yield. Considering years with baseline sediment production only, the measured and modeled sediment yields compared favorably, indicating that the latter processes primarily contribute during extreme events.

Soil erosion is a complex process involving multiple natural and anthropic agents, causing the deterioration of multiple components comprising soil health. Here, we provide an estimate of the spatial patterns of cropland susceptibility to erosion by sheet and rill, gully, wind, tillage, and root crops harvesting and report the co-occurrence of these processes using a multi-model approach. In addition, to give a global overview of potential future changes, we identify the locations where these multiple concurrent soil erosion processes may be expected to intersect with projected dry/wet climate changes by 2070. Of a modelled 1.48 billion hectares (B ha) of global cropland, our results indicate that 0.56 B ha (∼36% of the total area) are highly susceptible (classes 4 and 5) to a single erosion process, 0.27 B ha (∼18% of the total area) to two processes and 0.02 B ha (1.4% of the total area) to three or more processes. An estimated 0.82 B ha of croplands are susceptible to possible increases in water (0.68 B ha) and wind (0.14 B ha) erosion. We contend that the presented set of estimates represents a basis for enhancing our foundational knowledge on the geography of soil erosion at the global scale. The generated insight on multiple erosion processes can be a useful starting point for decision-makers working with ex-post and ex-ante policy evaluation of the UN Sustainable Development Goal 15 (Life on Land) activities. Scientifically, this work provides the hitherto most comprehensive assessment of soil erosion risks at the global scale, based on state-of-the-art models.

Abstract To evaluate the sustainability of biomass plantations, effects on soil organic carbon (SOC) need to be quantified. Miscanthus × giganteus is increasingly used as a bioenergy plant, and it has been hypothesized that, after conversion from cropland, Miscanthus cropping increases SOC storage, whereas conversion from grassland to Miscanthus provides, on average, no sequestration. All field studies hitherto were carried out on mineral soils with topsoil SOC contents of below 3.3%. Here, we analyze in the temperate zone of Switzerland five sites that have been cultivated with Miscanthus for 19–24 years and of which four sites are higher in topsoil SOC content (4.7%–16.2%) and storage (188–262 t SOC) than any previously studied Miscanthus plantation in Europe. We used the difference in carbon isotopic signature between C4 (Miscanthus) and neighboring plots with C3 vegetation (grassland) to quantify the accumulation of new SOC from Miscanthus down to 0.75 m. Annual C4‐C accumulation rates were 1.66 (standard error ± 0.14) t C4‐C ha−1 year−1 (range: 1.26–2.01) in the upper 0.3 m of soil and 1.96 (±0.18) t C4‐C ha−1 year−1 (1.40–2.38) in 0–0.75 m. Average rates for 0–0.3 m were higher than those of mineral soils (n = 37) published previously (0.96 [±0.10] t C4‐C ha−1 year−1). However, high rates of C4‐C accumulation were also reported previously for some mineral soils. Nevertheless, the one mineral soil in our study did not reveal a systematically different accumulation of Miscanthus‐derived carbon compared with the four carbon‐rich soils. We therefore conclude that soils rich in organic matter do not show a different C4‐C accumulation pattern as compared with mineral soils. However, their C4‐C accumulation rates are at the upper end of the data ensemble. Our results further underpin that conversion to Miscanthus, despite C4‐C accumulation, provides no means to increase soil carbon stocks relative to grassland management.

Peatlands drained for agriculture emit large amounts of nitrous oxide (N2O) and thereby contribute to global warming. In order to counteract soil subsidence and sustain agricultural productivity, mineral soil coverage of drained organic soil is an increasingly used practice. This management option may also influence soil-borne N2O emissions. Understanding the effect of mineral soil coverage on N2O emissions from agricultural peatland is necessary to implement peatland management strategies which not only sustain agricultural productivity but also reduce N2O emissions. In this study, we aimed to quantify the N2O emissions from an agriculturally managed peatland in Switzerland and to evaluate the effect of mineral soil coverage on these emissions. The study was conducted over two years on a grassland on drained nutrient-rich fen in the Swiss Rhine Valley which was divided into two parts, both with identical management. One site was not covered with mineral soil (reference “Ref”), and the other site had a ∼40 cm thick mineral soil cover (coverage “Cov”). The grassland was intensively managed, cut 5–6 times per year, and received c. 230 kg N ha−1 yr−1 of nitrogen fertilizer. N2O emissions were continuously monitored using an automatic time integrating chamber (ATIC) system. During the experimental period, site Ref released 20.5 ± 2.7 kg N ha−1 yr−1 N2O-N, whereas the N2O emission from site Cov was only 2.3 ± 0.4 kg N ha−1 yr−1. Peak N2O emissions were mostly detected following fertilizer application and lasted for 2–3 weeks before returning to the background N2O emissions. At both sites, N2O peaks related to fertilization events contributed more than half of the overall N2O emissions. However, not only the fertilization induced N2O peaks but also background N2O emissions were lower with mineral soil coverage. Our data suggest a strong and continued reduction in N2O emissions with mineral soil cover from the investigated organic soil. Mineral soil coverage, therefore, seems to be a promising N2O mitigation option for intensively used drained organic soils when a sustained use of the drained peatland for intensive agricultural production is foreseen, and potential rewetting and restoration of the peatland are not possible.

Phosphorus is an essential nutrient critical for agriculture, but because it is non-renewable its future availability is threatened. Here the authors show that across the globe most nations have net losses of phosphorus, with soil erosion as the major route of loss in Europe, Africa and South America.

Peatland degradation impairs soil functions such as carbon storage and the existence of biodiversity hotspots. Therefore, and in view of the ongoing climate change, an efficient method of evaluating peatland hydrology and the success of restoration efforts is needed. To understand the role of microbial groups in biogeochemical cycling, gaseous loss and isotopic fractionation that lead to specific isotopic depth patterns (δ13C, δ15N), we integrated previously published stable isotope data with a membrane fatty acid (mFA) analysis related to various microbial groups that are known to be common in peatlands. We performed two sampling campaigns to verify the observed stable isotope depth trends in nutrient-poor peatlands in Northern Europe. Cores were taken from adjacent drained (or rewetted) and undrained sites. Fungal-derived mFA abundance was highest in the uppermost part of the drained layer. We found increasing bacterial-derived mFA concentrations with depth peaking in the middle of the drained layers, which correlates with a δ15N peak of bulk material. The results support our hypothesis that changing peatland hydrology induce a shift in microbial community and metabolism processes and is therefore also imprinted in stable isotope values. Under waterlogged conditions overall levels of microbial-derived mFAs were generally low. Drained layers showed simultaneous changes in microbial abundance and composition and depth trends in stable isotope bulk values. Bacteria, particularly acidobacteria, can be expected to dominate increased denitrification with low oxygen saturation accompanied by increased δ15N bulk values in the remaining substrate. Interestingly, cores from recent rewetted peatlands show no depth trend of δ15N in the layers grown under rewetting conditions; this is congruent with relatively low concentrations of microbial-derived mFAs. Hence, we conclude that stable isotopes, especially δ15N values, reflect changing microbial metabolic processes, which differ between drained and undrained - and especially also for recent rewetted–peatlands. As today stable isotope measurements are routine measurements, these findings enable us to get cost- and time efficient reliable information of drainage and restoration success.

To give soils and soil degradation, which are among the most crucial threats to ecosystem stability, social and political visibility, small and large scale modelling and mapping of soil erosion is inevitable. The most widely used approaches during an 80year history of erosion modelling are Universal Soil Loss Equation (USLE)-type based algorithms which have been applied in 109 countries. Addressing soil erosion by water (excluding gully erosion and land sliding), we start this review with a statistical evaluation of nearly 2,000 publications). We discuss model developments which use USLE-type equations as basis or side modules, but we also address recent development of the single USLE parameters (R, K, LS, C, P). Importance, aim and limitations of model validation as well as a comparison of USLE-type models with other erosion assessment tools are discussed. Model comparisons demonstrate that the application of process-based physical models (e.g., WEPP or PESERA) does not necessarily result in lower uncertainties compared to more simple structured empirical models such as USLE-type algorithms. We identified four key areas for future research: (i) overcoming the principally different nature of modelled (gross) versus measured (net) erosion rates, in coupling on-site erosion risk to runoff patterns, and depositional regime, (ii) using the recent increase in spatial resolution of remote sensing data to develop process based models for large scale applications, (iii) strengthen and extend measurement and monitoring programs to build up validation data sets, and (iv) rigorous uncertainty assessment and the application of objective evaluation criteria to soil erosion modelling. Keywords: Universal soil loss equation, RUSLE, CSLE, Soil redistribution, Soil degradation, Water erosion, Model, Review

This study presents the first mapping of soil erosion risk modelling based on the Revised Universal Soil Loss Equation (RUSLE) at a sub-annual (monthly) temporal resolution and national scale (100 m spatial resolution). The monthly maps show highest water erosion rates on Swiss grasslands in August (1.25 t ha–1 month–1). In summer, the mean monthly soil loss by water erosion is 48 times higher than the mean soil loss in winter. Considering the annual average fraction of green vegetation cover of 54%, the predicted soil erosion rate for the Swiss national grassland area would add up to a total eroded soil mass of 5.26 Mt yr–1. The RUSLE application with an intact 100% vegetation cover would largely reduce the soil loss to an average annual rate of 0.14 t ha–1 year–1. These findings clearly highlight the importance to consider and maintain the current status of the vegetation cover for soil erosion prediction and soil conservation, respectively.

So far, neither a grassland map, temporal analysis of the conversion of permanent grassland (PG) to other land uses nor the differentiation of permanent and temporal grassland exists for Switzerland. For the first time in Switzerland, we present a Swiss national grassland map for the year 2015 capturing the extent of both, permanent and temporal grasslands (here called grasslands) by intersecting the information of three datasets. We blended the high temporal resolution Climate Change Initiate (CCI) Land Cover of 2015 (processed by the European Space Agency (ESA)), with the high spatial resolution Swiss topographical landscape model “SwissTLM3D” and the landscape model “vector25” both provided by Swisstopo. The final data presents the spatial patterns and the national extent of Swiss grasslands. Furthermore, the recently published (April 2017) CCI Land Cover dataset allow extracting the extent of grasslands for 24 years (1992–2015) with a coarse spatial resolution of 300 m. We used the time series data of the grassland extent to produce annual PG maps from 1996 to 2015. That data enables the identification of the development of grassland extent over two decades. The Swiss national grassland map is used for investigating the spatio-temporal patterns of the soil erosion risk of Swiss grasslands (see Mapping spatio-temporal dynamics of the cover and management factor (C-factor) for grasslands in Switzerland, doi:10.1016/j.rse.2018.04.008 (Schmidt el al., 2018)). Keywords: Land use change, Land cover classification, Time series, Change detection, Soil erosion, Alpine environment, C-factor, CCI Land Cover

Human activity and related land use change are the primary cause of soil erosion. Here, the authors show the impacts of 21st century global land use change on soil erosion based on an unprecedentedly high resolution global model that provides insights into the mitigating effects of conservation agriculture.

Abstract For the first time, speciation of Fe, Mn, Zn, Ni, Cu and Pb was determined along the profiles of 8 constructed wetlands (CWs) consisting of fluviatile sand (Fluv), clinopyroxene-dominated lava sand (Cl-LS) and zeolite-dominated lava sand (Ze-LS), aiming at quantifying metal behaviour in CWs and the impact caused by different filter materials. With the exception of Mn, which underwent reductive dissolution, CWs were sinks for the studied metals. Metal accumulation rates differed in the following order: Ze-LS ≥ Cl-LS > Fluv CWs, reflecting the highest metal adsorption capacity and the lowest hydraulic conductivity of Ze-LS. Sequential extraction data indicated the highest metal mobility (readily mobilised and adsorbed fractions summing up to ~60%) in Fluv CWs, implying a higher risk of metal release into adjacent environments if Fluv from CWs will be improperly disposed after usage. Zinc and Ni were transported into the deeper CW layers to a larger extent than Cu and Pb, reflecting adsorption affinity to all filter materials in the order of Pb > Cu > Zn > Ni. Therefore, understanding metal speciation and mobility in such materials is crucial when they are considered for application as filters in CWs.

Erosion in alpine grasslands is a major threat to ecosystem services of alpine soils. Natural causes for the occurrence of soil erosion are steep topography and prevailing climate conditions in combination with soil fragility. To increase our understanding of ongoing erosion processes and support sustainable land-use management, there is a need to acquire detailed information on spatial occurrence and temporal trends. Existing approaches to identify these trends are typically laborious, have lack of transferability to other regions, and are consequently only applicable to smaller regions. In order to overcome these limitations and create a sophisticated erosion monitoring tool capable of large-scale analysis, we developed a model based on U-Net, a fully convolutional neural network, to map different erosion processes on high-resolution aerial images (RGB, 0.25–0.5 m). U-Net was trained on a high-quality data set consisting of labeled erosion sites mapped with object-based image analysis (OBIA) for the Urseren Valley (Central Swiss Alps) for five aerial images (16 year period). We used the U-Net model to map the same study area and conduct quality assessments based on a held-out test region and a temporal transferability test on new images. Erosion classes are assigned according to their type (shallow landslide and sites with reduced vegetation affected by sheet erosion) or land-use impacts (livestock trails and larger management affected areas). We show that results obtained by OBIA and U-Net follow similar linear trends for the 16 year study period, exhibiting increases in total degraded area of 167% and 201%, respectively. Segmentations of eroded sites are generally in good agreement, but also display method-specific differences, which lead to an overall precision of 73%, a recall of 84%, and a F1-score of 78%. Our results show that U-Net is transferable to spatially (within our study area) and temporally unseen data (data from new years) and is therefore a method suitable to efficiently and successfully capture the temporal trends and spatial heterogeneity of degradation in alpine grasslands. Additionally, U-Net is a powerful and robust tool to map erosion sites in a predictive manner utilising large amounts of new aerial imagery.

Ice nucleating particles (INP) active at a few degrees below 0°C are produced by a range of organisms and released into the environment. They may affect cloud properties and precipitation when becoming airborne. So far, our knowledge about sources of biological INP is based on grab samples of vegetation, soil or water studied in the laboratory. By combining measurements of INP concentrations in river water with river water discharge rates over the course of 16 months, we obtained a lower limit for the production rate of INP in a watershed covering most of Switzerland (4 × 105 INP−8 m−2 d−1). Coincidentally, we found that INP−8 are likely to retain their potential for catalysing ice formation in the natural environment for at least several months before they are mobilized by an intensive rainfall, washed into the river and exported from the watershed.

As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES) and the respective mean annual R-factor map, the monthly aspect of rainfall erosivity has been added to REDES. Rainfall erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland) or former data density was not satisfactory (Austria, France, and Spain). The different time resolutions (from 5 to 60 min) of high temporal data require a conversion of monthly R-factor based on a pool of stations with available data at all time resolutions. Because the conversion factors show smaller monthly variability in winter (January: 1.54) than in summer (August: 2.13), applying conversion factors on a monthly basis is suggested. The estimated monthly conversion factors allow transferring the R-factor to the desired time resolution at a European scale. The June to September period contributes to 53% of the annual rainfall erosivity in Europe, with different spatial and temporal patterns depending on the region. The study also investigated the heterogeneous seasonal patterns in different regions of Europe: on average, the Northern and Central European countries exhibit the largest R-factor values in summer, while the Southern European countries do so from October to January. In almost all countries (excluding Ireland, United Kingdom and North France), the seasonal variability of rainfall erosivity is high. Very few areas (mainly located in Spain and France) show the largest from February to April. The average monthly erosivity density is very large in August (1.67) and July (1.63), while very small in January and February (0.37). This study addresses the need to develop monthly calibration factors for seasonal estimation of rainfall erosivity and presents the spatial patterns of monthly rainfall erosivity in European Union and Switzerland. Moreover, the study presents the regions and seasons under threat of rainfall erosivity.

Human activities have transformed a significant proportion of the world's land surface, with profound effects on ecosystem processes. Soil applications of macronutrients such as nitrate, phosphorus, potassium or calcium are routinely used in the management of croplands, grasslands and forests to improve plant health or increase productivity. However, while the effects of continuous fertilization and liming on terrestrial ecosystems are well documented, remarkably little is known about the legacy effect of historical fertilization and liming events in terrestrial ecosystems and of the mechanisms involved. Here, we show that more than 70 years after the last application of lime on a subalpine grassland, all major soil and plant calcium pools were still significantly larger in limed than in unlimed plots, and that the resulting shift in the soil calcium/aluminium ratio continues to affect ecosystem services such as primary production. The difference in the calcium content of the vegetation and the topmost 10 cm of the soil in limed vs. unlimed plots amounts to approximately 19.5 g m(-2), equivalent to 16.3% of the amount that was added to the plots some 70 years ago. In contrast, plots that were treated with nitrogen-phosphorus-potassium fertilizer in the 1930s did not differ from unfertilized plots in any of the soil and vegetation characteristics measured. Our findings suggest that the long-term legacy effect of historical liming is due to long-term storage of added calcium in stable soil pools, rather than a general increase in nutrient availability. Our results demonstrate that single applications of calcium in its carbonated form can profoundly and persistently alter ecosystem processes and services in mountain ecosystems.