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48 results for “soil erosion”
Long-term effectiveness of sustainable land management practices to control runoff, soil erosion, and nutrient loss and the role of rainfall intensity in Mediterranean rainfed agroecosystems
<p>This data set corresponds to the open-access article "Long-term effectiveness of sustainable land management practices to control runoff, soil erosion, and nutrient loss and the role of rainfall intensity in Mediterranean rainfed agroecosystems" published in CATENA. (<a href="https://doi.org/10.1016/j.catena.2019.104352">https://doi.org/10.1016/j.catena.2019.104352</a>), funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </p>
Data from: Assessing spatial patterns of soil erosion in a high‐latitude rangeland
<p>High‐latitude areas are experiencing rapid change: we therefore need a better understanding of the processes controlling soil erosion in these environments. We used a spatiotemporal approach to investigate soil erosion in Svalbarðstunga, Iceland (66° N, 15° W), a degraded rangeland. We used three complementary datasets: 1) high‐resolution UAV imagery collected from 12 sites (total area ~0.75 km<sup>2</sup>); 2) historical imagery of the same sites; and 3) a simple, spatially‐explicit cellular automata model. Sites were located along a gradient of increasing altitude and distance from the sea, and varied in erosion severity (5–47% eroded). We found that there was no simple relationship between location along the environmental gradient and the spatial characteristics of erosion. Patch‐size frequency distributions lacked a characteristic scale of variation, but followed a power‐law distribution on five of the 12 sites. Present total eroded area is poorly related to current, site‐scale levels of environmental stress, but the number of small erosion patches did reflect site‐level stress. Small (< 25 m<sup>2</sup>) erosion patches clustered near large patches. The model results suggested that the large‐scale patterns observed likely arise from strong, local interactions, which mean that erosion spreads from degraded areas. Our findings suggest that contemporary erosion patterns reflect historical stresses, as well as current environmental conditions. The importance of abiotic processes to the growth of large erosion patches and their relative insensitivity to current environmental conditions makes it likely that the total eroded area will continue to increase, despite a warming climate and reducing levels of grazing pressure.</p>
Fourier transformed infrared reflectance (FTIR) spectra of peat soils collected from the top and bottom of peatland erosion gullies
<p>Peat soil was randomly collected from gullies within two eroding blanket bogs. Balmoral (BAM) is on a large high-altitude plateau blanket bog in the eastern part of the Cairngorms National Park, Scotland, UK (56.93° N, − 3.16° E, 642 m asl) and Glensaugh (GSA) is an upland livestock farm with sections of and blanket bog peatland in the Grampian foothills (56.55° N, 2.33° E, 412 m asl). Both sites have undergone extensive degradation and peat erosion, and both have, in some parts, recently undergone restoration practices, including bunding and reprofiling.</p> <p>Peat samples were collected at Glensaugh and Balmoral as follows. At Glensaugh, peat at the top 1 cm of exposed gully sides (approximately 10-20cm from the vegetated surface) and at the gully bottom were taken, air dried and passed on for FTIR analysis. These gullies correspond to four erosion pin measurement areas and their corresponding peat sediment trap areas at Glensaugh. At Balmoral, the same approach was taken except six ‘gully top’ and ‘gully bottom’ sites were randomly selected and not geographically paired in the same way at Glensaugh.</p> <p>Samples were air dried and finely ball milled, prior to FTIR analysis. FTIR spectra were recorded using a Bruker Vertex 70 FTIR spectrometer (Bruker, Ettlingen, Germany) and OPUS 7.2 software. To record the FTIR spectra, each of the samples were placed, in turn, on a Diamond Attenuated Total Reflectance (DATR) sampling accessory, with a single reflectance system. Data points in the range of 4000-400 cm-1 were recorded with a resolution of 4 cm-1 and average of 64 scans. A spectrum of the empty sampling accessory, with the same resolution and number of scans, was recorded as the background spectrum before each measurement. </p> <p>Since the penetration depth for the DATR accessory is different for each wavelength and is directly proportional to the wavelength of the incident light (The higher the wavenumber the lower the penetration), an ATR correction was applied to the spectra to correct this effect, using the OPUS software. No correction was required for water vapour and CO2 as the spectrometer is continuously purged with dry air.</p> <p> </p> <p>In the dataset, columns correspond to the following:</p> <p>Site: Balmoral or Glensaugh</p> <p>Gully Position: Top or bottom</p> <p>Gully Number: Replicate gullies within the site</p> <p>Sample date: Date</p> <p>Sample ID: Unique identifier</p> <p>Remaining columns: Reflectance at a given wavelength</p>
Dataset of bank soil parameters and stochastic modelling of bank erosion processes in the Middle Yangtze River
<p><span>A probabilistic process-based model of bank erosion has proposed, </span><span>embedding the probability distributions of</span><span> different bank soil parameters. <span><span> The dataset includes the spatial distribution characteristics of critical shear stress, friction angle, and cohesion. The prediction results analyzed the effects of soil erosion resistance capacity and the variability of shear strength parameters in the simulation of bank erosion processes, obtaining the probability of mass failure and the distributions of bank erosion width. Additionally, the study investigated the effect of varying water content on bank erosion modeling and further analyzed how considering more influencing factors in the model affects prediction uncertainty and accuracy. Moreover, the relationship between the variability of these factors and river morphology was discussed. The dataset provides the aforementioned prediction results, and relevant plots were generated using MATLAB or Python, with the associated plotting code also uploaded.</span></span></span></p>
Effects of Grass Cover on the Overland Soil Erosion Mechanism under Simulated Rainfall
<p><span>Grass cover has a complex influence on overland soil erosion. This study quantified the impact of grass cover on overland soil erosion using a dimensionless water flow path index. It systematically analyzed the response mechanism among overland soil erosion, slope gradient, rainfall intensity, and hydrodynamic parameters, aiming to identify the optimal hydrodynamic parameters capable of characterizing overland soil erosion. A predictive model for soil erosion was constructed based on general dimensionless water flow intensity parameters, comprehensively evaluating the mechanism of soil erosion on grass-covered overland under simulated rainfall conditions. The results indicate that the model constructed using dimensionless parameters exhibits strong adaptability and can be effectively validated in other experiments.</span></p>
Gas, erosion, runoff and soil data and metadata derived from Diverfarming project
<p>Gas, erosion, runoff and soil data and metadata of the different cases studies and long terms from WP5 "Environmental impact and delivery of ecosystem services by crop diversification", derived from H2020 Diverfarming project. This workpackage has been designed to provide sound and robust scientific understanding of the benefits and drawbacks of the tailored diversified cropping systems for improvement of the environmental quality and delivery of ecosystem services in each pedoclimatic region. http://www.diverfarming.eu.</p>
Monitoring the effect of water erosion on soil surface microtopography using Lidar
<p>This dataset contains three dimensional (3D) reconstructions of laboratory soil surfaces eroding under rainfall simulation experiments. Experiments were conducted using the Walnut Gulch Rainfall Simulator at the USDA-ARS Southwest Watershed Research Center in Tucson, Arizona. A terrestrial laser scanner was used to collect soil surface 3D data.</p>
Input data for soil erosion practical
<p>This dataset is to be used for the soil erosion practical published at: https://github.com/wieka29/Soil-erosion-practical</p> <p> </p>
Measured properties in soil samples and marine sediment collected in Galion Bay (Martinique, France) in order to trace erosion sources in insular tropical catchments
<p>This dataset was compiled in order to select the optimal suite of tracers and identify and quantify the main sources of sediment deposited in Galion Bay and associated chlordecone transfers since the 1960s. It includes measured properties for potential sources collected across the Galion catchment (Martinique, France) and along a sediment core sampled in Galion Bay (GAL17-04, N°IGSN TOAE0000000573). Associated with this dataset, metadata are integrated for sources and targets registered using International Geological Sample Numbers (IGSN).</p>
Long-term soil erosion monitoring in China using the RUSLE model based on Google Earth Engine
<p>This dataset contains soil erosion maps in China at 500m resolution from 2010 to 2020. The dataset is stored in GeoTif format with the unit of t/(km<sup>2 </sup>· a).</p>
Incorporating biological soil crusts into the B factor to enhance the accuracy of soil erosion models in drylands
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Data from: Rates and processes of aeolian soil erosion in West Greenland
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Data from: Assessing spatial patterns of soil erosion in a high‐latitude rangeland
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Trade-off between vegetation type, soil erosion control and surface water in global semi-arid regions: A meta-analysis
<p>Soil erosion control and water resource protection can closely interact during restoration of terrestrial ecosystems. In semi‐arid ecosystems, an urgent issue is how vegetation restoration can achieve the goal of soil erosion mitigation and water conservation, which in turn, feeds back to ecosystem functioning.</p> <p>We reviewed 78 articles from 22 countries in semi‐arid areas to evaluate the effects of vegetation type (i.e. forest, grassland and scrubland) on runoff and sediment yields across different environmental conditions (i.e. vegetation coverage, rainfall intensity, slope gradient and soil texture).</p> <p>Our meta‐analysis shows that runoff and sediment reduction both increased as the vegetation coverage increased, and tended to be stable when vegetation coverage exceeded 60%. Vegetation provided a greater benefit for sediment reduction than for runoff control under intense rainfall. Grasslands were generally more effective in reducing sediment than other vegetation types. Forests, grasslands and scrublands were most efficient in soil erosion control on 20°–30°, 0°–25° and 10°–25° slopes respectively. Grasslands and scrublands generally performed better with respect to soil erosion control on moderately coarse soils, whereas forests were most effective on medium‐textured and moderately fine soils.</p> <p>Synthesis and applications. Effective restoration and soil erosion control in semi‐arid ecosystems strongly depends on the selection of vegetation type. Our study further indicates that, for land managers, it is critical to consider local slope, and soil texture, and maintain appropriate vegetation coverage to achieve ecosystem sustainability. Grasslands might be particularly suitable to optimize the trade‐off between soil erosion control and surface water resource in semi‐arid regions.</p>
Integrated Modeling of Flow, Soil Erosion, and Nutrient Dynamics in a Regional Watershed: Assessing Natural and Human-Induced Impacts
<p>Attached file is the verification data for the submitted manuscript "Integrated Modeling of Flow, Soil Erosion, and Nutrient Dynamics in a Regional Watershed: Assessing Natural and Human-Induced Impacts" .</p>
Soil Erosion on United States. Present and Future (2020-2050)
<p>Brought on by anthropogenic actions, accelerated soil erosion inflicts extreme changes in terrestrial and aquatic ecosystems. These field-scale (30 m) changes have neither been fully surveyed in the present, nor predicted for a probable future. Water-driven soil erosion (<em>i.e</em>., sheet and rill erosion) rates across the contiguous United States were estimated for the present, and then predicted for the future using three alternative Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (2.6, 4.5, and 8.5) of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The G2 erosion model which is integrated with Machine Learning (ML) and Remote Sensing (RS) techniques were used to estimate soil erosion based on gauge observations of long-term precipitation, and climate and land use land cover (LULC) scenarios. The baseline model (2020) estimated soil erosion rates of 2.32 Mg ha<sup>−1</sup> yr<sup>−1</sup> under current conservation agriculture practices (CPs). Maintaining current CPs, future scenarios predict an 8 % to 21 % increase in soil erosion under different combinations of SSP-RCP climate and LULC change scenarios. The findings of this study can help policy makers for future conservation planning on maintaining soil fertility, mitigating environmental impacts, and promoting food security.</p> <p> </p> <p>This reprository provide the soil erosion maps for united states in present and future.</p>
Soil Erosion and Sediment Yield Modeling - Southern Caspian Sea River Basins
<p>Soil Erosion and Sediment Yield Modeling - Southern Caspian Sea River Basins</p>
Data from: Reducing soil erosion by improving community functional diversity in semi-arid grasslands
1. Great efforts have been made to control soil erosion by restoring plant communities in degraded ecosystems world-wide. However, soil erosion has not been substantially reduced mainly because current restoration strategies lead to large areas of mono-specific vegetation, which are inefficient in reducing soil erosion because of their simple canopy and root structure. Therefore, an advanced understanding of how community functional composition affects soil erosion processes, as well as an improved restoration scheme to reduce soil erosion, is urgently needed. 2. We investigated the effect of community functional composition on soil erosion in restored semi-arid grasslands on the Loess Plateau of China. Community functional composition of 16 restored grasslands was quantified by community-weighted mean (CWM) and functional diversity (FD) trait values, which were calculated from nine plant functional traits of thirteen locally dominant plant species. Species richness and evenness were also measured. Soil erosion rates were measured using standard erosion plots. The multimodel inference approach was used to estimate the direction and the relative importance of these biodiversity indices in reducing soil erosion. 3. A robust and strong negative effect of functional divergence (FDiv) on soil erosion was found. The prevalence of particular trait combinations can also decrease soil erosion. The greatest control over soil erosion was exerted when the community mean root diameter was small and the root tensile strength was great. 4. Synthesis and applications: These findings imply that community functional diversity plays an important role in reducing soil erosion in semi-arid restored grasslands. This means that current restoration strategies can be greatly improved by incorporating community functional diversity into restoration design. We propose a trait-based restoration framework for reducing soil erosion, termed 'SSM' (Screening–Simulating–Maintaining). SSM aims to translate the target of community functional diversity into community assemblages that can be manipulated by practitioners. Based on this framework, a comprehensive procedure, highlighting functional diversity as the primary concern in determining optimal community assemblages, was developed to meet the pressing need for more effective restoration strategies to reduce soil erosion.
Trade-off between vegetation type, soil erosion control and surface water in global semi-arid regions: A meta-analysis
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Data from: Reducing soil erosion by improving community functional diversity in semi-arid grasslands
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OpenNeuro
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