Land use and climate change impacts on global soil erosion by water (2015-2070)

Soil erosion is a major global soil degradation threat to land, freshwater, and oceans. Wind and water are the major drivers, with water erosion over land being the focus of this work; excluding gullying and river bank erosion. Improving knowledge of the probable future rates of soil erosion, accelerated by human activity, is important both for policy makers engaged in land use decision-making and for earth-system modelers seeking to reduce uncertainty on global predictions. Here we predict future rates of erosion by modeling change in potential global soil erosion by water using three alternative (2.6, 4.5, and 8.5) Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. Global predictions rely on a high spatial resolution Revised Universal Soil Loss Equation (RUSLE)-based semiempirical modeling approach (GloSEM). The baseline model (2015) predicts global potential soil erosion rates of 43+9.2−7 Pg yr−1, with current conservation agriculture (CA) practices estimated to reduce this by ∼5%. Our future scenarios suggest that socioeconomic developments impacting land use will either decrease (SSP1-RCP2.6–10%) or increase (SSP2-RCP4.5 +2%, SSP5-RCP8.5 +10%) water erosion by 2070. Climate projections, for all global dynamics scenarios, indicate a trend, moving toward a more vigorous hydrological cycle, which could increase global water erosion (+30 to +66%). Accepting some degrees of uncertainty, our findings provide insights into how possible future socioeconomic development will affect soil erosion by water using a globally consistent approach. This preliminary evidence seeks to inform efforts such as those of the United Nations to assess global soil erosion and inform decision makers developing national strategies for soil conservation

GloSEM: high-resolution global estimates of present and future soil displacement in croplands by water erosion

Healthy soil is the foundation underpinning global agriculture and food security. Soil erosion is currently the most serious threat to soil health, leading to yield decline, ecosystem degradation and economic impacts. Here, we provide high-resolution (ca. 100 × 100 m) global estimates of soil displacement by water erosion obtained using the Revised-Universal-Soil-Loss-Equation-based Global Soil Erosion Modelling (GloSEM) platform under present (2019) and future (2070) climate scenarios (i.e. Shared Socioeconomic Pathway [SSP]1–Representative Concentration Pathway [RCP]2.6, SSP2–RCP4.5 and SSP5–RCP8.5). GloSEM is the first global modelling platform to take into account regional farming systems, the mitigation effects of conservation agriculture (CA), and climate change projections. We provide a set of data, maps and descriptive statistics to support researchers and decision-makers in exploring the extent and geography of soil erosion, identifying probable hotspots, and exploring (with stakeholders) appropriate actions for mitigating impacts. In this regard, we have also provided an Excel spreadsheet that can provide useful insights into the potential mitigating effects of present and future alternative CA scenarios at the country level

EUSEDcollab: a network of data from European catchments to monitor net soil erosion by water

As a network of researchers we release an open-access database (EUSEDcollab) of water discharge and suspended sediment yield time series records collected in small to medium sized catchments in Europe. EUSEDcollab is compiled to overcome the scarcity of open-access data at relevant spatial scales for studies on runoff, soil loss by water erosion and sediment delivery. Multi-source measurement data from numerous researchers and institutions were harmonised into a common time series and metadata structure. Data reuse is facilitated through accompanying metadata descriptors providing background technical information for each monitoring station setup. Across ten European countries, EUSEDcollab covers over 1600 catchment years of data from 245 catchments at event (11 catchments), daily (22 catchments) and monthly (212 catchments) temporal resolution, and is unique in its focus on small to medium catchment drainage areas (median = 43 km(2), min = 0.04 km(2), max = 817 km(2)) with applicability for soil erosion research. We release this database with the aim of uniting people, knowledge and data through the European Union Soil Observatory (EUSO)

Species-specific, pan-European diameter increment models based on data of 2.3 million trees

ResearchBackground: Over the last decades, many forest simulators have been developed for the forests of individual European countries. The underlying growth models are usually based on national datasets of varying size, obtained from National Forest Inventories or from long-term research plots. Many of these models include country- and location-specific predictors, such as site quality indices that may aggregate climate, soil properties and topography effects. Consequently, it is not sensible to compare such models among countries, and it is often impossible to apply models outside the region or country they were developed for. However, there is a clear need for more generically applicable but still locally accurate and climate sensitive simulators at the European scale, which requires the development of models that are applicable across the European continent. The purpose of this study is to develop tree diameter increment models that are applicable at the European scale, but still locally accurate. We compiled and used a dataset of diameter increment observations of over 2.3 million trees from 10 National Forest Inventories in Europe and a set of 99 potential explanatory variables covering forest structure, weather, climate, soil and nutrient deposition. Results: Diameter increment models are presented for 20 species/species groups. Selection of explanatory variables was done using a combination of forward and backward selection methods. The explained variance ranged from 10% to 53% depending on the species. Variables related to forest structure (basal area of the stand and relative size of the tree) contributed most to the explained variance, but environmental variables were important to account for spatial patterns. The type of environmental variables included differed greatly among species. Conclusions: The presented diameter increment models are the first of their kind that are applicable at the European scale. This is an important step towards the development of a new generation of forest development simulators that can be applied at the European scale, but that are sensitive to variations in growing conditions and applicable to a wider range of management systems than before. This allows European scale but detailed analyses concerning topics like CO2 sequestration, wood mobilisation, long term impact of management, etcinfo:eu-repo/semantics/publishedVersio

Are current dynamic water quality models too complex? A comparison of a new parsimonious phosphorus model, SimplyP, and INCA-P

Catchment-scale water quality models are becoming increasingly popular tools for exploring the potential effects of land management, land use change and climate change on water quality. However, the dynamic, catchment-scale nutrient models in common usage are complex, with many uncertain parameters requiring calibration, limiting their usability and robustness. A key question is whether this complexity is justified. To explore this, we have developed a parsimonious P model, SimplyP, incorporating a coupled rainfall-runoff model and a biogeochemical model able to simulate streamflow, suspended sediment, particulate and dissolved P dynamics. The model’s complexity is compared in a small rural catchment in northeast Scotland. For three land use classes, less than six SimplyP model parameters must be determined through calibration alone, the rest may be based on measurements; INCA-P has around 40 unmeasurable parameters. Despite simpler process-representation, SimplyP produced a slightly better dissolved P simulation during both calibration and validation, and produced similar long-term projections in response to changes in land management. Results support the hypothesis that INCA-P is overly complex for the study catchment. We hope our findings will help prompt wider model comparison exercises, as well as debate amongst the water quality modelling community as to whether today's models are fit for purpose. Simpler models such as SimplyP have the potential to be useful management and research tools, building blocks for future model development (prototype code is freely available), or benchmarks against which more complex models could be evaluated

Soil erosion modelling: A global review and statistical analysis

To gain a better understanding of the global application of soil erosion prediction models, we comprehensivelyreviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the re-gions 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 per-form this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. Theresulting database, named‘Global Applications of Soil Erosion Modelling Tracker (GASEMT)’, includes 3030 indi-vidual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluatedand transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insightsinto the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to sup-port the upcoming country-based United Nations global soil-erosion assessment in addition to helping to informsoil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is anopen-source database available to the entire user-community to develop research, rectify errors, andmakefutureexpansion

Soil erosion modelling: A bibliometric analysis

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\u27s 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

Metabolic synergies in the biotransformation of organic and metallic toxic compounds by a saprotrophic soil fungus

The saprotrophic fungus Penicillium griseofulvum was chosen as model organism to study responses to a mixture of hexachlorocyclohexane (HCH) isomers (α-HCH, β-HCH, γ-HCH, δ-HCH) and of potentially toxic metals (vanadium, lead) in solid and liquid media. The P. griseofulvum FBL 500 strain was isolated from polluted soil containing high concentrations of HCH isomers and potentially toxic elements (Pb, V). Experiments were performed in order to analyse the tolerance/resistance of this fungus to xenobiotics, and to shed further light on fungal potential in inorganic and organic biotransformations. The aim was to examine the ecological and bioremedial potential of this fungus verifying the presence of mechanisms that allow it to transform HCH isomers and metals under different, extreme, test conditions. To our knowledge, this work is the first to provide evidence on the biotransformation of HCH mixtures, in combination with toxic metals, by a saprotrophic non-white-rot fungus and on the metabolic synergies involved

Maternal prepregnancy body mass index and offspring white matter microstructure: results from three birth cohorts

Prepregnancy maternal obesity is a global health problem and has been associated with offspring metabolic and mental ill-health. However, there is a knowledge gap in understanding potential neurobiological factors related to these associations. This study explored the relation between maternal prepregnancy body mass index (BMI) and offspring brain white matter microstructure at the age of 6, 10, and 26 years in three independent cohorts. Maternal BMI was associated with higher FA and lower MD in multiple brain tracts in offspring aged 10 and 26 years, but not at 6 years of age. Future studies should examine whether our observations can be replicated and explore the potential causal nature of the findings.This work was supported by the European Union’s Horizon 2020 research and innovation program [grant agreement no. 633595 DynaHEALTH] and no. 733206 LifeCycle], the Netherlands Organization for Health Research and Development [ZONMW Vici project 016.VICI.170.200]. The PREOBE cohort was funded by Spanish Ministry of Innovation and Science. Junta de Andalucía: Excellence Projects (P06-CTS-02341) and Spanish Ministry of Economy and Competitiveness (BFU2012-40254-C03-01). The first phase of the Generation R Study is made possible by financial support from the Erasmus Medical Centre, the Erasmus University, and the Netherlands Organization for Health Research and Development (ZonMW, grant ZonMW Geestkracht 10.000.1003). The Northern Finland Birth Cohort 1986 is funded by University of Oulu, University Hospital of Oulu, Academy of Finland (EGEA), Sigrid Juselius Foundation, European Commission (EURO-BLCS, Framework 5 award QLG1-CT-2000-01643), NIH/NIMH (5R01MH63706:02