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.

Land management policy shift influenced seasonal variation of erosion-induced nitrogen and phosphorus outputs from intensive agricultural catchment.

A shift in policy to intensive agricultural production and land management often leads to excessive fertilizer application and accelerated erosion with consequent detrimental effects to water bodies. We investigated the impact of that shift by quantifying the spatial and temporal change in sediment sources and associated total nitrogen (TN) and total phosphorus (TP) pollutants output loads in an intensive agricultural catchment in North China across one year (November 2021-November 2022). We describe the implications of this work for intensive agriculture elsewhere in China and other countries. Seasonal sediment source apportionment was estimated at the catchment outlet using Berillium-7 (7Be) combined with compound-specific stable isotope (CSSI) signatures from sources and sediments. Diagnostic 'fingerprints' in MixSIAR were used to discriminate sediment sources between forest and crop farmland converted from forest (F + C(F)), crop farmland (C), and vegetable farmland (V). Our study identified F + C(F) as the dominant sediment source (mean 55.24 ± 2.91 %), intermediate on V (mean 30.06 ± 2.20 %), and least on C (mean 14.70 ± 2.13 %). Sedimentation ranged from 37.98 ± 3.02 to 89.60 ± 12.68 t·ha-1·event-1 and coincided with shifted land use policy and rainfall distribution. The TN and TP in sediment were both mainly derived from F + C(F) (averaged 22.27 ± 4.26 t·event-1 and 11.62 ± 2.28 t·event-1) and least from V (averaged 1.63 ± 0.29 and 2.09 ± 0.33 t·event-1). Despite being a significant sediment source, V contributed little sediment TN and TP input for eutrophication. Our findings imply that F + C(F) are diffuse sources of catchment pollution over the short term. These results describe the successful use of CSSI and 7Be to cost-effectively quantify the seasonal variation of sediment TN and TP loads from land-use-specific sources in the catchment under shifting land management policy in China with potential for use elsewhere. These findings enable soil conservation strategies and land management practices optimized for implementing targeted pollutant abatement initiatives in intensive agriculture in China and elsewhere.

A new framework for evaluating dust emission model development using dichotomous satellite observations of dust emission.

Dust models are essential for understanding the impact of mineral dust on Earth's systems, human health, and global economies, but dust emission modelling has large uncertainties. Satellite observations of dust emission point sources (DPS) provide a valuable dichotomous inventory of regional dust emissions. We develop a framework for evaluating dust emission model performance using existing DPS data before routine calibration of dust models. To illustrate this framework's utility and arising insights, we evaluated the albedo-based dust emission model (AEM) with its areal (MODIS 500 m) estimates of soil surface wind friction velocity (us∗) and common, poorly constrained grain-scale entrainment threshold (u∗ts) adjusted by a function of soil moisture (H). The AEM simulations are reduced to its frequency of occurrence, P(us∗>u∗tsH). The spatio-temporal variability in observed dust emission frequency is described by the collation of nine existing DPS datasets. Observed dust emission occurs rarely, even in North Africa and the Middle East, where DPS frequency averages 1.8 %, (~7 days y-1), indicating extreme, large wind speed events. The AEM coincided with observed dust emission ~71.4 %, but simulated dust emission ~27.4 % when no dust emission was observed, while dust emission occurrence was over-estimated by up to 2 orders of magnitude. For estimates to match observations, results showed that grain-scale u∗ts needed restricted sediment supply and compatibility with areal us∗. Failure to predict dust emission during observed events, was due to us∗ being too small because reanalysis winds (ERA5-Land) were averaged across 11 km pixels, and inconsistent with us∗ across 0.5 km pixels representing local maxima. Assumed infinite sediment supply caused the AEM to simulate dust emission whenever P(us∗>u∗tsH), producing false positives when wind speeds were large. The dust emission model scales of existing parameterisations need harmonising and a new parameterisation for u∗ts is required to restrict sediment supply over space and time.

Satellites reveal Earth's seasonally shifting dust emission sources.

Establishing mineral dust impacts on Earth's systems requires numerical models of the dust cycle. Differences between dust optical depth (DOD) measurements and modelling the cycle of dust emission, atmospheric transport, and deposition of dust indicate large model uncertainty due partially to unrealistic model assumptions about dust emission frequency. Calibrating dust cycle models to DOD measurements typically in North Africa, are routinely used to reduce dust model magnitude. This calibration forces modelled dust emissions to match atmospheric DOD but may hide the correct magnitude and frequency of dust emission events at source, compensating biases in other modelled processes of the dust cycle. Therefore, it is essential to improve physically based dust emission modules. Here we use a global collation of satellite observations from previous studies of dust emission point source (DPS) dichotomous frequency data. We show that these DPS data have little-to-no relation with MODIS DOD frequency. We calibrate the albedo-based dust emission model using the frequency distribution of those DPS data. The global dust emission uncertainty constrained by DPS data (±3.8 kg m-2 y-1) provides a benchmark for dust emission model development. Our calibrated model results reveal much less global dust emission (29.1 ± 14.9 Tg y-1) than previous estimates, and show seasonally shifting dust emission predominance within and between hemispheres, as opposed to a persistent North African dust emission primacy widely interpreted from DOD measurements. Earth's largest dust emissions, proceed seasonally from East Asian deserts in boreal spring, to Middle Eastern and North African deserts in boreal summer and then Australian shrublands in boreal autumn-winter. This new analysis of dust emissions, from global sources of varying geochemical properties, have far-reaching implications for current and future dust-climate effects. For more reliable coupled representation of dust-climate projections, our findings suggest the need to re-evaluate dust cycle modelling and benefit from the albedo-based parameterisation.

Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models.

Soil erosion by water is accelerated by a warming climate and negatively impacts water security and ecological conservation. The Tibetan Plateau (TP) has experienced warming at a rate approximately twice that observed globally, and heavy precipitation events lead to an increased risk of erosion. In this study, we assessed current erosion on the TP and predicted potential soil erosion by water in 2050. The study was conducted in three steps. During the first step, we used the Revised Universal Soil Equation (RUSLE), publicly available data, and the most recent earth observations to derive estimates of annual erosion from 2002 to 2016 on the TP at 1-km resolution. During the second step, we used a multiple linear regression (MLR) model and a set of climatic covariates to predict rainfall erosivity on the TP in 2050. The MLR was used to establish the relationship between current rainfall erosivity data and a set of current climatic and other covariates. The coefficients of the MLR were generalised with climate covariates for 2050 derived from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models to estimate rainfall erosivity in 2050. During the third step, soil erosion by water in 2050 was predicted using rainfall erosivity in 2050 and other erosion factors. The results show that the mean annual soil erosion rate on the TP under current conditions is 2.76tha-1y-1, which is equivalent to an annual soil loss of 559.59×106t. Our 2050 projections suggested that erosion on the TP will increase to 3.17tha-1y-1 and 3.91tha-1y-1 under conditions represented by RCP2.6 and RCP8.5, respectively. The current assessment and future prediction of soil erosion by water on the TP should be valuable for environment protection and soil conservation in this unique region and elsewhere.

Lateral transport of soil carbon and land-atmosphere CO2 flux induced by water erosion in China.

Soil erosion by water impacts soil organic carbon stocks and alters CO2 fluxes exchanged with the atmosphere. The role of erosion as a net sink or source of atmospheric CO2 remains highly debated, and little information is available at scales larger than small catchments or regions. This study attempts to quantify the lateral transport of soil carbon and consequent land-atmosphere CO2 fluxes at the scale of China, where severe erosion has occurred for several decades. Based on the distribution of soil erosion rates derived from detailed national surveys and soil carbon inventories, here we show that water erosion in China displaced 180 ± 80 Mt C⋅y(-1) of soil organic carbon during the last two decades, and this resulted a net land sink for atmospheric CO2 of 45 ± 25 Mt C⋅y(-1), equivalent to 8-37% of the terrestrial carbon sink previously assessed in China. Interestingly, the "hotspots," largely distributed in mountainous regions in the most intensive sink areas (>40 g C⋅m(-2)⋅y(-1)), occupy only 1.5% of the total area suffering water erosion, but contribute 19.3% to the national erosion-induced CO2 sink. The erosion-induced CO2 sink underwent a remarkable reduction of about 16% from the middle 1990s to the early 2010s, due to diminishing erosion after the implementation of large-scale soil conservation programs. These findings demonstrate the necessity of including erosion-induced CO2 in the terrestrial budget, hence reducing the level of uncertainty.

Soil organic carbon dust emission: an omitted global source of atmospheric CO2.

Soil erosion redistributes soil organic carbon (SOC) within terrestrial ecosystems, to the atmosphere and oceans. Dust export is an essential component of the carbon (C) and carbon dioxide (CO(2)) budget because wind erosion contributes to the C cycle by removing selectively SOC from vast areas and transporting C dust quickly offshore; augmenting the net loss of C from terrestrial systems. However, the contribution of wind erosion to rates of C release and sequestration is poorly understood. Here, we describe how SOC dust emission is omitted from national C accounting, is an underestimated source of CO(2) and may accelerate SOC decomposition. Similarly, long dust residence times in the unshielded atmospheric environment may considerably increase CO(2) emission. We developed a first approximation to SOC enrichment for a well-established dust emission model and quantified SOC dust emission for Australia (5.83 Tg CO(2)-e yr(-1)) and Australian agricultural soils (0.4 Tg CO(2)-e yr(-1)). These amount to underestimates for CO(2) emissions of ≈10% from combined C pools in Australia (year = 2000), ≈5% from Australian Rangelands and ≈3% of Australian Agricultural Soils by Kyoto Accounting. Northern hemisphere countries with greater dust emission than Australia are also likely to have much larger SOC dust emission. Therefore, omission of SOC dust emission likely represents a considerable underestimate from those nations' C accounts. We suggest that the omission of SOC dust emission from C cycling and C accounting is a significant global source of uncertainty. Tracing the fate of wind-eroded SOC in the dust cycle is therefore essential to quantify the release of CO(2) from SOC dust to the atmosphere and the contribution of SOC deposition to downwind C sinks.

Uncertainty in soil carbon accounting due to unrecognized soil erosion.

The movement of soil organic carbon (SOC) during erosion and deposition events represents a major perturbation to the terrestrial carbon cycle. Despite the recognized impact soil redistribution can have on the carbon cycle, few major carbon accounting models currently allow for soil mass flux. Here, we modified a commonly used SOC model to include a soil redistribution term and then applied it to scenarios which explore the implications of unrecognized erosion and deposition for SOC accounting. We show that models that assume a static landscape may be calibrated incorrectly as erosion of SOC is hidden within the decay constants. This implicit inclusion of erosion then limits the predictive capacity of these models when applied to sites with different soil redistribution histories. Decay constants were found to be 15-50% slower when an erosion rate of 15 t soil ha(-1)  yr(-1) was explicitly included in the SOC model calibration. Static models cannot account for SOC change resulting from agricultural management practices focused on reducing erosion rates. Without accounting for soil redistribution, a soil sampling scheme which uses a fixed depth to support model development can create large errors in actual and relative changes in SOC stocks. When modest levels of erosion were ignored, the combined uncertainty in carbon sequestration rates was 0.3-1.0 t CO2  ha(-1)  yr(-1) . This range is similar to expected sequestration rates for many management options aimed at increasing SOC levels. It is evident from these analyses that explicit recognition of soil redistribution is critical to the success of a carbon monitoring or trading scheme which seeks to credit agricultural activities.

Microbial hitchhikers on intercontinental dust: catching a lift in Chad.

Ancient mariners knew that dust whipped up from deserts by strong winds travelled long distances, including over oceans. Satellite remote sensing revealed major dust sources across the Sahara. Indeed, the Bodélé Depression in the Republic of Chad has been called the dustiest place on earth. We analysed desert sand from various locations in Chad and dust that had blown to the Cape Verde Islands. High throughput sequencing techniques combined with classical microbiological methods showed that the samples contained a large variety of microbes well adapted to the harsh desert conditions. The most abundant bacterial groupings in four different phyla included: (a) Firmicutes-Bacillaceae, (b) Actinobacteria-Geodermatophilaceae, Nocardiodaceae and Solirubrobacteraceae, (c) Proteobacteria-Oxalobacteraceae, Rhizobiales and Sphingomonadaceae, and (d) Bacteroidetes-Cytophagaceae. Ascomycota was the overwhelmingly dominant fungal group followed by Basidiomycota and traces of Chytridiomycota, Microsporidia and Glomeromycota. Two freshwater algae (Trebouxiophyceae) were isolated. Most predominant taxa are widely distributed land inhabitants that are common in soil and on the surfaces of plants. Examples include Bradyrhizobium spp. that nodulate and fix nitrogen in Acacia species, the predominant trees of the Sahara as well as Herbaspirillum (Oxalobacteraceae), a group of chemoorganotrophic free-living soil inhabitants that fix nitrogen in association with Gramineae roots. Few pathogenic strains were found, suggesting that African dust is not a large threat to public health.