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14 results for “Water erosion”
SERENA EJPSoil Soil loss by water erosion of Tuscany (Italy)
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>One of the objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The present data was prepared according to the methodology of the SERENA Soil erosion and soil erosion control cookbook. Soil loss was used as an indicator for soil erosion (ST). The map of soil loss by water erosion (soil threat) was based on the RUSLE model. For Italy, the cookbook was applied in the Tuscany region. <br> <br>To create the soil loss map we used:</p> <ul> <li>for R-factor, not freely available database of meteorological parameters spatialized at 250 m (minimum and maximum daily air temperature; cumulate daily precipitation) over Tuscany region (period 1990–2022, Lamma Consortium) and a local linear equation between R and mean annual precipitation (P);</li> <li>for C -factor, Regional Land use map 1:10.000 (2018, freely available at: https://www502.regione.toscana.it/geoscopio/usocoperturasuolo.html) and ESDAC method (https://doi.org/10.1016/j.landusepol.2015.05.021) ; </li> <li>for K-factor, sand, silt, clay, and O.C. (%) maps (built from 4.000 soil profiles, following FAO’s methodology in GSP-GSOC map, Lamma Consortium), and Torri et al. (1997) function;</li> <li>for LS-factor, DEM 10 m of Tuscany, (freely available at https://www502.regione.toscana.it/geoscopio/cartoteca.html99) and Desmet & Govers (1996) SAGA tool (applied at 10 m and upscaled);</li> <li>for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor of 0.5 was considered, based on expert evaluation)</li> </ul> <p>Maps was delivered in the GeoTIFF format in the resolution of 100m. <br>Delivered data will be validated by stakeholders from Italy (scientist) in October, 2024.</p>
Data and ancillary data for publication: Natural infrastructure and water erosion mitigation in the Andes
<p>The data contain information on the effectiveness of natural infrastructure to mitigate soil erosion. Data were compiled from 118 case studies from the Andean region, whereby information on natural infrastructure interventions, soil erosion and soil quality were tabulated and analysed.</p> <p>The data contains the following documents:<br> -Database with data on soil erosion, soil quality for different types of natural infrastructure (118 case studies)<br> -Metadata<br> -Summary of terms used in the systematic review of the literature (in Spanish and English)<br> -List of bibliographic data sources that were searched with the search terms<br> -Full bibliographic references of all 118 case studies</p> <p><strong>Full reference </strong></p> <p><em>Vanacker V, Molina A, Rosas-Barturen M, Bonnesoeur V, Román-Dañobeytia F, Ochoa-Tocachi B, Buytaert W (2022). The effect of natural infrastructure on water erosion mitigation in the Andes. </em></p> <p> </p> <p> </p>
Water sample analysis and satellite imagery of a thermo-erosion gully and its surroundings in Adventdalen, Svalbard.
<h2><strong>Data description</strong></h2> <p>This dataset is part of the supplemental information to the paper "Rapid Ice-Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff" by Parmentier et al. (2024). It includes the analysis of water quality in and around a thermo-erosion gully on the high-Arctic archipelago of Svalbard, and three satellite images that give an overview of the wider area around this gully in the context of a snow fence experiment (Cooper et al. 2011). More details are provided in Parmentier et al. (2024).</p> <h2><strong>Background</strong></h2> <p>Thicker snow cover in permafrost areas causes deeper active layers and thaw subsidence, which alter local hydrology and may amplify the loss of soil carbon. However, the potential for changes in snow cover and surface runoff to mobilize permafrost carbon remains poorly quantified. The data presented here is part of a study that showed that a snow fence experiment on High-Arctic Svalbard inadvertently led to surface subsidence through warming, and extensive downstream erosion due to increased surface runoff. Within a decade of artificially-raised snow depths, several ice wedges collapsed, forming a 50 m long and 1.5 m deep thermo-erosion gully in the landscape. We estimate that 1.1 to 3.3 tons C may have eroded, and that the gully is a hotspot for processing of mobilised aquatic carbon. Our study show that interactions among snow, runoff and permafrost thaw form an important driver of soil carbon loss.</p> <h2><strong>Water samples</strong></h2> <p>The following datafile includes the analysis of several water samples taken in and near a thermo-erosion gully on Svalbard on August 5<sup>th</sup> and 6<sup>th</sup>, 2017. These were analyzed for dissolved organic carbon (DOC), particulate organic carbon (POC), particulate nitrogen (PN) content, and stable carbon isotope ratios δ<sup>13</sup>C-DOC and δ<sup>13</sup>C-POC. In addition, temperature, pH, oxygen, and electrical conductivity were measured in the field on the day of sampling. This data is provided in the following Excel file that also includes the latitude and longitude for each sample point: </p> <ul> <li>Parmentier et al - 2024 - Water Sample Analysis.xlsx</li> </ul> <h3><strong> </strong><strong>Sample analysis</strong></h3> <p>A full description of the analysis is repeated here from the supplemental information in the accompanying publication (Parmentier et al. 2024). The water samples were filtered on the day of collection through a pre-combusted glass fiber filter with pore size of 0.7 µm (Whatman, Grade GF/F). After filtration, the filters were packed in aluminum foil and frozen for later analysis of the collected particulate matter. From the filtrate, three samples of ~50 ml were taken and immediately frozen for transport.</p> <p>The filtered water samples were analyzed for their dissolved organic carbon (DOC) content and their stable carbon isotope ratio δ<sup>13</sup>C-DOC. This combined analysis was carried out at the labs of UCLouvain, Belgium with an Aurora 1030W TOC Carbon Analyzer, from OI Analytical, coupled to an IRMS (Thermo delta V Advantage). In the Aurora 1030W, the water samples were purged with H<sub>3</sub>PO<sub>4</sub>(phosphoric acid) to remove any dissolved inorganic carbon (DIC). Afterwards, Na<sub>2</sub>S<sub>2</sub>O<sub>8</sub> (sodium persulfate) was added to the heated sample (97 °C) to oxidize any DOC to CO<sub>2</sub>. With N<sub>2</sub> as the carrier gas, the CO<sub>2</sub> was transferred to the analyzing units where the total concentration and δ<sup>13</sup>C-DOC of the CO<sub>2</sub> were detected. The δ<sup>13</sup>C-DOC samples were calibrated against the certified standard IAEA-CH-6 (-10.449 ± 0.033 ‰VPDB) and an internal sucrose standard (-26.99 +/- 0.04 ‰). The DOC measurements were calibrated against a concentration range (n=8) of the same standards (Morana et al., 2015).</p> <p> The particulate matter retained on the filters was analyzed for particulate organic carbon (POC) and particulate nitrogen (PN) concentrations, as well as δ<sup>13</sup>C-POC. The glass fiber filters were subsampled and repeatedly acidified with HCl (1.5 M) in pre-combusted Ag capsules to remove carbonates. Analyses were performed at the Stable Isotope Facility of the University of California in Davis using an Elementar Vario EL Cube (Elementar Analysensysteme GmbH, Hanau, Germany) connected to a PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK). Isotope ratios of δ<sup>13</sup>C are reported relative to the international standard VPDB (Vienna PeeDee Belemnite).</p> <h2><strong>Satellite imagery</strong></h2> <p>To show the development of the thermo-erosion gully over time, we provide three high resolution satellite images from the Digital Globe constellation of satellites. The areal extent of these images covers the entire snow fence experiment in the valley of Adventdalen on Svalbard. They were acquired on August 5<sup>th</sup>, 2011, August 30<sup>th</sup>, 2013, and July 9<sup>th</sup>, 2015 by the WorldView-2, GeoEye-1 and WorldView-3 satellites, respectively. These images are provided as GeoTiffs – projected in the UTM 33X coordinate system:</p> <ul> <li>SnoEco_2011AUG05_WV2_MUL_Pansharpened_bco_rcs_dobj.tif</li> <li>SnoEco_2013AUG30_GE1_MUL_Pansharpened_bco_rcs_dobj.tif</li> <li>SnoEco_2015JUL09_WV3_MUL_Pansharpened_bco_rcs_dobj.tif</li> </ul> <p>Each of these files includes the following color bands: </p> <ul> <li>Band 1: Blue</li> <li>Band 2: Green</li> <li>Band 3: Red</li> <li>Band 4: Near Infrared</li> </ul> <p>In addition, the images are clipped to the following coordinate bounds (in UTM 33X):</p> <ul> <li> <p><span>x<sub>min</sub>, x<sub>max</sub></span><span>: 523740, 524825</span></p> </li> <li> <p><span>y<sub>min</sub>, y<sub>max</sub></span><span>: 8677150, 8678100</span></p> </li> </ul> <p>For full details on these satellite products, we refer to DigitalGlobe/Maxar.<strong> </strong></p> <h3><strong>Image processing</strong></h3> <p>The satellite imagery was processed according to DigitalGlobe guidelines and calibration coefficient adjustment factors. The radiometrically corrected source images were first converted to top-of-the-atmosphere spectral radiance, and thereafter to top-of-the-atmosphere reflectance. Following this processing, each color band of the image was pansharpened (using Bicubic interpolation) with the RCS algorithm in the Orfeo ToolBox of QGIS 2.18 to increase the horizontal resolution to ~50 cm. To reduce haze effects, the images were further corrected through a dark object subtraction (bottom 1 percentile of the blue band) which was applied to each band separately. Subsequent negative values were set to zero.<strong> </strong></p> <h2><strong>Acknowledgments</strong></h2> <p>This research was funded by the Research Council of Norway (RCN; grant agreement 230970), and the FRAM - Terrestrial flagship (362255 and 642018). F.J.W.P. and S.W. received additional funding from the RCN (grant agreement 323945). The high-resolution satellite imagery comes courtesy of the DigitalGlobe Foundation. We thank UCLouvain and the University of California, Davis for assisting in the sample analysis.<strong> </strong></p> <h2><strong>References</strong></h2> <p>Cooper, E. J., Dullinger, S., & Semenchuk, P. (2011). Late snowmelt delays plant development and results in lower reproductive success in the High Arctic. <em>Plant Science</em>, 180(1), 157–167. https://doi.org/10.1016/j.plantsci.2010.09.005</p> <p>Morana, C., Darchambeau, F., Roland, F. A. E., Borges, A. V., Muvundja, F., Kelemen, Z., et al. (2015). Biogeochemistry of a large and deep tropical lake (Lake Kivu, East Africa: insights from a stable isotope study covering an annual cycle. <em>Biogeosciences</em>, 12(16), 4953–4963. https://doi.org/10.5194/bg-12-4953-2015</p> <p>Parmentier, F. J. W., Nilsen, L, Tømmervik, H., Meisel, O. H., Bröder, L., Vonk, J. E., Westermann, S., Semenchuk, P. R., Cooper, E. J., Rapid Ice-Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff, <em>Geophysical Research Letters</em>, In press</p>
Soil erosion by water in the 1980s-2020s in the steppe region of the southeast of the East European Plain (Volgograd region, Russia)
<p>The dataset contains rasters with a 30m resolution of the distribution of soil erosion by water. Raster "Soil Losses 1980s" has shown the average soil losses by water erosion in the 1980-1990s, and raster "Soil Losses 2020s" has shown average soil losses by water erosion in the 2010-2020s.</p>
Numerical model of the Messinian Mediterranean combining hydrological water balance, river erosion, and flexural isostasy: TISC code and input dataset for the Lago-Mare
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ICON-Coast model output for a study on the impact of Arctic coastal erosion on sea water carbonate saturation.
<p>Primary output of the ocean-biogeochemistry model ICON-Coast that has been used to create the figures in a manuscript on the impact of Arctic coastal erosion on sea water carbonate saturation.</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>
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>
Water Erosion Risk in the Eastern Rift Valley: Application of RUSLE Modeling in the Kenyan Great Rift Valley Region
<p>RUSLE Model Parameters for Estimating Water Erosion Risk in the Eastern Rift Valley: Application of RUSLE Modeling in the Kenyan Great Rift Valley Region</p>
SAWEMD: South Asian Water Erosion Modeled Dataset
<p><strong>SAWEMD: A State-of-the-Art Water Erosion Modeled Dataset for South Asia.</strong></p> <p>Water erosion is a pressing environmental issue in South Asia, exacerbated by climate change. SAWEMD, the South Asia Water Erosion Modeled Dataset, addresses this challenge by identifying potential erosion hotspots and assessing future risks under various climate scenarios using the Revised Universal Soil Loss Equation (RUSLE). This dataset integrates high-resolution data on climate, land use, harvested crop areas, soil types, and elevation for both current and future periods. Future projections are based on CMIP6 climate and land use datasets, evaluated under two distinct Shared Socio-economic Pathways (SSP126 and SSP585).</p> <p> </p> <p>SAWEMD is the first open-access water erosion dataset for South Asia, providing crucial information and related parameters for researchers and policymakers. The comprehensive data and methodology used to create this high-resolution dataset are detailed in the following publication:</p> <p> </p> <p><strong>Das, Subhankar, Manoj Kumar Jain, and Vivek Gupta. "An assessment of anticipated future changes in water erosion dynamics under climate and land use change scenarios in South Asia." Journal of Hydrology 637 (2024): 131341. https://doi.org/10.1016/j.jhydrol.2024.131341</strong></p>
Trade-off between vegetation type, soil erosion control and surface water in global semi-arid regions: A meta-analysis
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PROTECTION OF IRRIGATED AREAS FROM WATER EROSION
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Water Erosion Risk Assessment in the Eastern Rift Valley: The Case of Kenya Great Rift Valley Region
<p>RUSLE Supplementary Materials </p> <p> </p> <p>Water Erosion Risk Assessment in the Eastern Rift Valley: The Case of Kenya Great Rift Valley Region</p>
Water and Wind Erosion in Australia during the past two decades
<p>Datesets of water and wind erosion in Australia using RUSLE and RWEQ model during the past two decades</p>
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OpenNeuro
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