The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data.

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.

A unifying modelling of multiple land degradation pathways in Europe.

Land degradation is a complex socio-environmental threat, which generally occurs as multiple concurrent pathways that remain largely unexplored in Europe. Here we present an unprecedented analysis of land multi-degradation in 40 continental countries, using twelve dataset-based processes that were modelled as land degradation convergence and combination pathways in Europe's agricultural (and arable) environments. Using a Land Multi-degradation Index, we find that up to 27%, 35% and 22% of continental agricultural (~2 million km2) and arable (~1.1 million km2) lands are currently threatened by one, two, and three drivers of degradation, while 10-11% of pan-European agricultural/arable landscapes are cumulatively affected by four and at least five concurrent processes. We also explore the complex pattern of spatially interacting processes, emphasizing the major combinations of land degradation pathways across continental and national boundaries. Our results will enable policymakers to develop knowledge-based strategies for land degradation mitigation and other critical European sustainable development goals.

An integrated modeling approach for estimating monthly global rainfall erosivity.

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001-2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission's Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha-1 h-1 month-1 in June-August over the Northern Hemisphere and ~ 700 MJ mm ha-1 h-1 month-1 in December-February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December-February and June-August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10-30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Deforestation as an Anthropogenic Driver of Mercury Pollution.

Deforestation reduces the capacity of the terrestrial biosphere to take up toxic pollutant mercury (Hg) and enhances the release of secondary Hg from soils. The consequences of deforestation for Hg cycling are not currently considered by anthropogenic emission inventories or specifically addressed under the global Minamata Convention on Mercury. Using global Hg modeling constrained by field observations, we estimate that net Hg fluxes to the atmosphere due to deforestation are 217 Mg year-1 (95% confidence interval (CI): 134-1650 Mg year-1) for 2015, approximately 10% of global primary anthropogenic emissions. If deforestation of the Amazon rainforest continues at business-as-usual rates, net Hg emissions from the region will increase by 153 Mg year-1 by 2050 (CI: 97-418 Mg year-1), enhancing the transport and subsequent deposition of Hg to aquatic ecosystems. Substantial Hg emissions reductions are found for two potential cases of land use policies: conservation of the Amazon rainforest (92 Mg year-1, 95% CI: 59-234 Mg year-1) and global reforestation (98 Mg year-1, 95% CI: 64-449 Mg year-1). We conclude that deforestation-related emissions should be incorporated as an anthropogenic source in Hg inventories and that land use policy could be leveraged to address global Hg pollution.

Pervasive soil phosphorus losses in terrestrial ecosystems in China.

Future phosphorus (P) shortages could seriously affect terrestrial productivity and food security. We investigated the changes in topsoil available P (AP) and total P (TP) in China's forests, grasslands, paddy fields, and upland croplands during the 1980s-2010s based on substantial repeated soil P measurements (63,220 samples in the 1980s, 2000s, and 2010s) and machine learning techniques. Between the 1980s and 2010s, total soil AP stock increased with a small but significant rate of 0.13 kg P ha-1 year-1 , but total soil TP stock declined substantially (4.5 kg P ha-1 year-1 ) in the four ecosystems. We quantified the P budgets of soil-plant systems by harmonizing P fluxes from various sources for this period. Matching trends of soil contents over the decades with P budgets and fluxes, we found that the P-surplus in cultivated soils (especially in upland croplands) might be overestimated due to the great soil TP pool compared to fertilization and the substantial soil P losses through plant uptake and water erosion that offset the P additions. Our findings of P-deficit in China raise the alarm on the sustainability of future biomass production (especially in forests), highlight the urgency of P recycling in croplands, and emphasize the critical role of country-level basic data in guiding sound policies to tackle the global P crises.

A global dataset on phosphorus in agricultural soils.

Numerous drivers such as farming practices, erosion, land-use change, and soil biogeochemical background, determine the global spatial distribution of phosphorus (P) in agricultural soils. Here, we revised an approach published earlier (called here GPASOIL-v0), in which several global datasets describing these drivers were combined with a process model for soil P dynamics to reconstruct the past and current distribution of P in cropland and grassland soils. The objective of the present update, called GPASOIL-v1, is to incorporate recent advances in process understanding about soil inorganic P dynamics, in datasets to describe the different drivers, and in regional soil P measurements for benchmarking. We trace the impact of the update on the reconstructed soil P. After the update we estimate a global averaged inorganic labile P of 187 kgP ha-1 for cropland and 91 kgP ha-1 for grassland in 2018 for the top 0-0.3 m soil layer, but these values are sensitive to the mineralization rates chosen for the organic P pools. Uncertainty in the driver estimates lead to coefficients of variation of 0.22 and 0.54 for cropland and grassland, respectively. This work makes the methods for simulating the agricultural soil P maps more transparent and reproducible than previous estimates, and increases the confidence in the new estimates, while the evaluation against regional dataset still suggests rooms for further improvement.

How the EU Soil Observatory contributes to a stronger soil erosion community.

New policy developments have emerged in relation to soil conservation after 2020. The Common Agricultural Policy (CAP) 2023-2027, the proposal for a Soil Monitoring Law and the mission 'A Soil Deal for Europe' have shaped a new policy framework at EU level, which requires updated assessments on soil erosion and land degradation. The EU Soil Observatory (EUSO) successfully organised a scientific workshop on 'Soil erosion for the EU' in June 2022. The event has seen the participation of more than 330 people from 63 countries, addressing important topics such as (i) management practices, (ii) large scale modelling, (iii) the importance of sediments in nutrient cycle, (vi) the role of landslides and (v) laying the foundations for early career scientists. As a follow up, among the 120 abstracts submitted in the workshop, we received fifteen manuscripts, out of which nine were selected for publication in the present special issue. In this editorial, we summarize the major challenges that the soil erosion research community faces in relation to supporting the increasing role of soils in the EU Green Deal.

Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolution.

Here, we present and release the Global Rainfall Erosivity Database (GloREDa), a multi-source platform containing rainfall erosivity values for almost 4000 stations globally. The database was compiled through a global collaboration between a network of researchers, meteorological services and environmental organisations from 65 countries. GloREDa is the first open access database of rainfall erosivity (R-factor) based on hourly and sub-hourly rainfall records at a global scale. This database is now stored and accessible for download in the long-term European Soil Data Centre (ESDAC) repository of the European Commission's Joint Research Centre. This will ensure the further development of the database with insertions of new records, maintenance of the data and provision of a helpdesk. In addition to the annual erosivity data, this release also includes the mean monthly erosivity data for 94% of the GloREDa stations. Based on these mean monthly R-factor values, we predict the global monthly erosivity datasets at 1 km resolution using the ensemble machine learning approach (ML) as implemented in the mlr package for R. The produced monthly raster data (GeoTIFF format) may be useful for soil erosion prediction modelling, sediment distribution analysis, climate change predictions, flood, and natural disaster assessments and can be valuable inputs for Land and Earth Systems modelling.

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 km2, min = 0.04 km2, max = 817 km2) 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).

Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network.

PURPOSE: The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. METHODS: Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A "traveling brains" dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. RESULTS: The results show an inter-center and inter-subject variability of <10%, except for "clustering coefficient" (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners' hardware. CONCLUSIONS: The results show low variability of connectivity topological metrics across sites running a harmonised protocol.

Projected landscape-scale repercussions of global action for climate and biodiversity protection.

Land conservation and increased carbon uptake on land are fundamental to achieving the ambitious targets of the climate and biodiversity conventions. Yet, it remains largely unknown how such ambitions, along with an increasing demand for agricultural products, could drive landscape-scale changes and affect other key regulating nature's contributions to people (NCP) that sustain land productivity outside conservation priority areas. By using an integrated, globally consistent modelling approach, we show that ambitious carbon-focused land restoration action and the enlargement of protected areas alone may be insufficient to reverse negative trends in landscape heterogeneity, pollination supply, and soil loss. However, we also find that these actions could be combined with dedicated interventions that support critical NCP and biodiversity conservation outside of protected areas. In particular, our models indicate that conserving at least 20% semi-natural habitat within farmed landscapes could primarily be achieved by spatially relocating cropland outside conservation priority areas, without additional carbon losses from land-use change, primary land conversion or reductions in agricultural productivity.

An assessment of South American sediment fluxes under climate changes.

Climate change can affect all levels of society and the planet. Recent studies have shown its effects on sediment fluxes in several locations worldwide, which can impact ecosystems and infrastructure such as reservoirs. In this study, we focused on simulating sediment fluxes using projections of future climate change for South America (SA), a continent with a high sediment transport rate to the oceans. Here, we used four climate change data yielded by the Eta Regional Climate Model: Eta-BESM, Eta-CanESM2, Eta-HadGEM2-ES, and Eta-MIROC5. In addition, it was evaluated the RCP4.5 greenhouse gas emissions scenario from CMIP5, which represents a moderate scenario. Climate change data between 1961 and 1995 (past) and 2021 and 2055 (future) were used to simulate and compare changes that may occur in water and sediment fluxes using the hydrological-hydrodynamic and sediment model MGB-SED AS. The Eta climate projections provided input data to MGB-SED AS model, such as precipitation, air surface temperature, incident solar radiation, relative humidity, wind speed, and atmospheric pressure. Our results showed sediment fluxes are expected to reduce (increase) in north-central (south-central) SA. While a sediment transport (QST) increase >30 % might occur, a 28 % decrease is expected to occur in the water discharge for the main SA basins. The most significant QST reductions were estimated for the Doce (-54 %), Tocantins (-49 %), and Xingu (-34 %) rivers, while the most significant increases were estimated for the Upper Paraná (409 %), Juruá (46 %), and Uruguay (40 %) rivers. We also observed that different climate change signals over large basins can impact the river water composition, which could lead to a new composition of the Amazon basin waters in the future, accompanied by a significant increase in sediment concentration.

Agricultural practices drive elevated rates of topsoil decline across Kenya, but terracing and reduced tillage can reverse this.

As agricultural land area increases to feed an expanding global population, soil erosion will likely accelerate, generating unsustainable losses of soil and nutrients. This is critical for Kenya where cropland expansion and nutrient loading from runoff and erosion is contributing to eutrophication of freshwater ecosystems and desertification. We used the Revised Universal Soil Loss Equation (RUSLE) to predict soil erosion rates under present land cover and potential natural vegetation nationally across Kenya. Simulating natural vegetation conditions allows the degree to which erosion rates are elevated under current land use practices to be determined. This methodology exploits new digital soil maps and two vegetation cover maps to model topsoil (top 20 cm) erosion rates, lifespans (the mass of topsoil divided by erosion rate), and lateral nutrient fluxes (nutrient concentration times erosion rate) under both scenarios. We estimated the mean soil erosion rate under current land cover at ~5.5 t ha-1 yr-1, ~3 times the rate estimated for natural vegetation cover (~1.8 t ha-1 yr-1), and equivalent to ~320 Mt yr-1 of topsoil lost nationwide. Under present erosion rates, ~8.8 Mt, ~315 Kt, and ~ 110 Kt of soil organic carbon, nitrogen and phosphorous are lost from soil every year, respectively. Further, 5.3 % of topsoils (~3.1 Mha), including at >25 % of croplands, have short lifespans (<100 years). Additional scenarios were tested that assume combinations of terracing and reduced tillage practices were adopted on croplands to mitigate erosion. Establishing bench terraces with zoned tillage could reduce soil losses by ≥75 %; up to 87.1 t ha-1 yr-1. These reductions are comparable to converting croplands to natural vegetation, demonstrating most agricultural soils can be conserved successfully. Extensive long-term monitoring of croplands with terraces and reduced tillage established is required to verify the efficacy of these agricultural support practices as indicated by our modelling.

Progress and challenges in sustainable land management initiatives: A global review.

Sustainable land management (SLM) is widely recognized as the key to reducing rates of land degradation, and preventing desertification. Many efforts have been made worldwide by various stakeholders to adopt and/or develop various SLM practices. Nevertheless, a comprehensive review on the spatial distribution, prospects, and challenges of SLM practices and research is lacking. To address this gap, we gathered information from a global SLM database provided by the World Overview of Conservation Approaches and Technologies (WOCAT) and two bibliographic databases of academic research. Over 1900 SLM practices and 1181 academic research papers from 129 and 90 countries were compiled and analyzed. Relatively better SLM dissemination was observed in dry subhumid countries and countries with medium scores on the Human Development Index (HDI), whereas dissemination and research were both lower in humid countries with low HDI values. Cropland was the main land use type targeted in both dissemination and research; degradation caused by water erosion and mitigation aimed at water erosion were also the main focus areas. Other dominant land use types (e.g., grazing) and SLM purposes (e.g., economic benefits) have received relatively less research attention compared to their dissemination. Overall, over 75 % of the 60 countries experiencing high soil erosion rates (>10 t ha-1 yr-1) also have low HDI scores, as well as poor SLM dissemination and research implying the limited evidence-based SLM dissemination in these countries. The limitation of research evidence can be addressed in the short term through integrating existing scientific research and SLM databases by adopting the proposed Research Evidence for SLM framework. There is, however, a great need for additional detailed studies of country-specific SLM challenges and prospects to create appropriate evidence-based SLM dissemination strategies to achieve multiple SLM benefits.

Retaining natural vegetation to safeguard biodiversity and humanity.

Global efforts to deliver internationally agreed goals to reduce carbon emissions, halt biodiversity loss, and retain essential ecosystem services have been poorly integrated. These goals rely in part on preserving natural (e.g., native, largely unmodified) and seminatural (e.g., low intensity or sustainable human use) forests, woodlands, and grasslands. To show how to unify these goals, we empirically derived spatially explicit, quantitative, area-based targets for the retention of natural and seminatural (e.g., native) terrestrial vegetation worldwide. We used a 250-m-resolution map of natural and seminatural vegetation cover and, from this, selected areas identified under different international agreements as being important for achieving global biodiversity, carbon, soil, and water targets. At least 67 million km2 of Earth's terrestrial vegetation (∼79% of the area of vegetation remaining) required retention to contribute to biodiversity, climate, soil, and freshwater conservation objectives under 4 United Nations' resolutions. This equates to retaining natural and seminatural vegetation across at least 50% of the total terrestrial (excluding Antarctica) surface of Earth. Retention efforts could contribute to multiple goals simultaneously, especially where natural and seminatural vegetation can be managed to achieve cobenefits for biodiversity, carbon storage, and ecosystem service provision. Such management can and should co-occur and be driven by people who live in and rely on places where natural and sustainably managed vegetation remains in situ and must be complemented by restoration and appropriate management of more human-modified environments if global goals are to be realized.

Retención de la vegetación natural para salvaguardar la biodiversidad y la humanidad Resumen Hoy en día hay muy poca integración de los esfuerzos mundiales para alcanzar los objetivos internacionales de reducción de las emisiones de carbono, impedimento de la pérdida de biodiversidad y conservación de los servicios ambientales esenciales. Estos objetivos dependen parcialmente de la conservación de los bosques, selvas y praderas naturales (por ejemplo, nativos y en su mayoría sin alteraciones) y seminaturales (por ejemplo, de uso humano sostenible o de baja intensidad). Obtuvimos de manera empírica objetivos espacialmente explícitos, cuantitativos y basados en áreas para la conservación de la vegetación terrestre natural y seminatural (por ejemplo, nativa) en todo el mundo para mostrar cómo unificar los objetivos internacionales. Usamos un mapa de 250 m de resolución de la cubierta vegetal natural y seminatural y, a partir de él, seleccionamos las áreas identificadas como importantes en diferentes acuerdos internacionales para alcanzar los objetivos globales de biodiversidad, carbono, suelo y agua. Al menos 67 millones de km2 de la vegetación terrestre de la Tierra (∼79% de la superficie de vegetación restante) requieren ser conservados para contribuir a los objetivos de conservación de la biodiversidad, el clima, el suelo y el agua dulce en virtud de cuatro de las resoluciones de las Naciones Unidas. Esto equivale a conservar la vegetación natural y seminatural en al menos el 50% de la superficie terrestre total de la Tierra (sin contar a la Antártida). Los esfuerzos de retención podrían contribuir a alcanzar múltiples objetivos simultáneamente, especialmente en donde la vegetación natural y seminatural puede gestionarse para lograr beneficios colaterales para la biodiversidad, el almacenamiento de carbono y la provisión de servicios ambientales. Esta gestión puede y debe ser impulsada y llevada a cabo por las personas que viven en y dependen de los lugares donde la vegetación natural y gestionada de forma sostenible permanece in situ y debe complementarse con la restauración y la gestión adecuada de entornos modificados por el hombre si se quieren alcanzar los objetivos globales.

Sources of copper into the European aquatic environment.

Chemical contamination from point source discharges in developed (resource-rich) countries has been widely regulated and studied for decades; however, diffuse sources are largely unregulated and widespread. In the European Union (EU), large dischargers report releases of some chemicals, yet little is known of total emissions (point and diffuse) and their relative significance. We estimated copper loadings from all significant sources including industry, sewage treatment plants, surface runoff (from traffic, architecture, and atmospheric deposition), septic tanks, agriculture, mariculture, marine transport (antifoulant leaching), and natural processes. A combination of European datasets, literature, and industry data were used to generate export coefficients. These were then multiplied by activity rates to derive loads. A total of approximately 8 kt of copper per annum (ktpa) is estimated to enter freshwaters in the EU, and another 3.5 ktpa enters transitional and coastal waters. The main inputs to freshwater are natural processes (3.7 ktpa), agriculture (1.8 ktpa), and runoff (1.8 ktpa). Agricultural emissions are dominated by copper-based plant protection products and farmyard manure. Urban runoff is influenced by copper use in architecture and by vehicle brake linings. Antifoulant leaching from boats (3.2 ktpa) dominates saline water loads of copper. It is noteworthy that most of the emissions originate in a limited number of copper uses where environmental exposure and pathways exist, compared with the bulk of copper use within electrical and electronic equipment and infrastructure that has no environmental pathway during its use. A sensitivity analysis indicated significant uncertainty in data from abandoned mines and urban runoff load estimates. This study provided for the first time a methodology and comprehensive metal load apportionment to European aquatic systems, identifying data gaps and uncertainties, which may be refined over time. Source apportionments using this methodology can inform more cost-effective environmental risk assessment and management. Integr Environ Assess Manag 2023;19:1031-1047. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Improving the phosphorus budget of European agricultural soils.

Despite phosphorus (P) being crucial for plant nutrition and thus food security, excessive P fertilization harms soil and aquatic ecosystems. Accordingly, the European Green Deal and derived strategies aim to reduce P losses and fertilizer consumption in agricultural soils. The objective of this study is to calculate a soil P budget, allowing the quantification of the P surpluses/deficits in the European Union (EU) and the UK, considering the major inputs (inorganic fertilizers, manure, atmospheric deposition, and chemical weathering) and outputs (crop production, plant residues removal, losses by erosion) for the period 2011-2019. The Land Use/Cover Area frame Survey (LUCAS) topsoil data include measured values for almost 22,000 samples for both available and total P. With advanced machine learning models, we developed maps for both attributes at 500 m resolution. We estimated the available P for crops at a mean value of 83 kg ha-1 with a clear distinction between North and South. The ratio of available P to the total P is about 1:17. The inorganic fertilizers and manure contribute almost equally as P inputs (mean 16 ± 2 kg P ha-1 yr-1 at 90 % confidence level) to agricultural soils, with high regional variations depending on farming practices, livestock density, and cropping systems. The P outputs came mainly from the exportation by the harvest of crop products and residues (97.5 %) and, secondly, by erosion. Using a sediment distribution model, we quantified the P fluxes to river basins and sea outlets. In the EU and UK, we estimated an average surplus of 0.8 kg P ha-1 yr-1 with high variability between countries with some regional variations. The P annual budget at regional scale showed ample possibility to improve P management by both reducing inputs in regions with high surplus (and P soil available) and rebalancing fertilization in those at risk of soil fertility depletion.

Global assessment of storm disaster-prone areas.

BACKGROUND: Advances in climate change research contribute to improved forecasts of hydrological extremes with potentially severe impacts on human societies and natural landscapes. Rainfall erosivity density (RED), i.e. rainfall erosivity (MJ mm hm-2 h-1 yr-1) per rainfall unit (mm), is a measure of rainstorm aggressiveness and a proxy indicator of damaging hydrological events. METHODS AND FINDINGS: Here, using downscaled RED data from 3,625 raingauges worldwide and log-normal ordinary kriging with probability mapping, we identify damaging hydrological hazard-prone areas that exceed warning and alert thresholds (1.5 and 3.0 MJ hm-2 h-1, respectively). Applying exceedance probabilities in a geographical information system shows that, under current climate conditions, hazard-prone areas exceeding a 50% probability cover ~31% and ~19% of the world's land at warning and alert states, respectively. CONCLUSION: RED is identified as a key driver behind the spatial growth of environmental disruption worldwide (with tropical Latin America, South Africa, India and the Indian Archipelago most affected).

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.

Structural Brain Network Reproducibility: Influence of Different Diffusion Acquisition and Tractography Reconstruction Schemes on Graph Metrics.

Background: Graph metrics of structural brain networks demonstrate to be a powerful tool for investigating brain topology at a large scale. However, the variability of the results related to applying different magnetic resonance acquisition schemes and tractography reconstruction techniques is not fully characterized. Materials and Methods: The present work aims to evaluate the influence of different combinations of diffusion acquisition schemes (single and multishell), diffusion models (tensor and spherical deconvolution), and tractography reconstruction approaches (deterministic and probabilistic) on the reproducibility of graph metrics derived from structural connectome on test/retest (TRT) data released by the Human Connectome Project. From each implemented experimental setup, both global and local graph metrics were evaluated and their reproducibility was estimated by the intraclass correlation coefficient (ICC). Moreover, the percentage relative standard deviation (pRSD) from the ICC values of local graph metrics was calculated to quantify how much the reproducibility varied across nodes within each experimental setup. Results: The presented results show that different combinations of diffusion acquisition schemes, diffusion models, and tractography algorithms can strongly affect the reproducibility of global and local graph metrics. The combination of constrained spherical deconvolution (CSD) and deterministic tractography gave generally high reproducibility (ICCs >0.75) and lowest pRSD for the considered graph metrics, meanwhile probabilistic CSD with a high b-value returned the highest reproducibility. Notably, the introduction of streamline selection filters on CSD can substantially affect the reproducibility. Discussion: This work demonstrates that the TRT reproducibility of graph metrics is generally high but can vary substantially with different combinations of acquisition and reconstruction schemes. Impact statement This work demonstrates the influence of different diffusion acquisition schemes, diffusion models, and tractography reconstruction approaches on the reproducibility of graph metrics derived from structural connectome. The presented findings impact on the choice of both acquisition protocol and processing pipeline for topological analyses to produce reproducible measurements for brain network studies.