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476 results for “Erosive”
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra greenhouse simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under greenhouse conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra nitrogen fertilized simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under nitrogen fertilization conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra nitrogen and phosphorus fertilization simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under nitrogen and phosphorus fertilization conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra phosphorus fertilization simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under phosphorus fertilization conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra shade house simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under shade conditions.
Pan‐Arctic Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw
<p>The datasets are issued from the combination of records of the ESA EO4PAC and Permafrost_cci and HORIZON 2020 Nunataryuk projects. The EO4PAC project aimed to develop a new generation of geospatial products for the observation of permafrost and associated changes from space with a special focus on the coastal Arctic. Four components were considered in the creation of the datasets:</p> <p>(1) Landsat-7/8 for the detection of coastline changes over the 2000-2020 period (Tanguy et al., 2024).</p> <p>(2) Sentinel-1/2 for the detection and mapping of coastal infrastructures (Bartsch et al. 2024), updating Wang et al. (2021).</p> <p>(3) Permafrost_cci timeseries for retrieval of trends of ground temperature and active layer thickness for the 2000-2020 period (Obu et al. 2021a,b), evaluated based on Martin et al (2023) and CALM et al. (2024).</p> <p>(4) Sea level rise by 2100 (Garner et al. 2022).</p> <p>The respective output provides a consistent mapping of settlements along arctic and permafrost-dominated coasts (2), and associated coastline and permafrost conditions changes during the last 20 years (1, 3). Combined together, an assessment of Arctic infrastructures at risk due to permafrost change (GT, ALT) and coastline erosion was possible, the latter with projections for the years 2030, 2050 and 2100.<a name="_heading=h.jkogrw14ymt"></a></p> <p>References</p> <p>Bartsch, Annett, Pointner, Georg, & Nitze, Ingmar. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (Version 2) [Data set]. Zenodo. https://zenodo.org/records/10160636.</p> <p>CALM, GTN-P, Wieczorek, M., Heim, B., Streletskiy, D., Bartsch, A., 2024, GTN-P CALM: 34 years of Active Layer Thickness (ALT) across latitudinal and elevational gradients in the Northern Hemisphere [dataset]. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.972777</p> <p>Garner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., et al. (2022). IPCC AR6 sea level projections [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6382554">https://doi.org/10.5281/zenodo.6382554</a></p> <p>Martin, Julia; Boike, Julia; Chadburn, Sarah; Zwieback, Simon; Anselm, Norbert; Goldau, Maybrit; Hammar, Jennika; Abramova, Ekatarina N; Lisovski, Simeon; Coulombe, Stéphanie; Dakin, Brampton; Wilcox, Evan James; Giamberini, Mariasilvia; Rader, Fieke; Suominen, Otso; Rudd, Daniel Alexander; Mastepanov, Mikhail; Young, Amanda (2023): T-MOSAiC 2021 myThaw data set [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.956039, In: Boike, Julia; Hammar, Jennika; Goldau, Maybrit; Miesner, Frederieke; Anselm, Norbert (2024): Circumarctic seasonal measurements of permafrost parameters (thaw depth, snow depth, vegetation and tree height, water level and soil properties) [dataset publication series]. PANGAEA, https://doi.org/10.1594/PANGAEA.971787</p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Tanguy, R., Bartsch, A., Nitze, I., Irrgang, A., Petzold, P., Widhalm, B., von Baeckmann, C., Boike, J., Martin, J., Efimova, A., Vieira, G., Whalen, D., Heim, B., Wieszorek, M., Grosse, G.: Pan‐Arctic Assessment of Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw, Earth’s Future, 10.1029/2024EF005013.</p> <p>Wang, S., Ramage, J., Bartsch, A., & Efimova, A. (2021). Population in the Arctic Circumpolar Permafrost Region at settlement level (Version 2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4529610" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.4529610</a></p>
SERENA EJPSOIL SK SOIL Erosion ErosionControl
<div> <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> <div> <p><span><span>The present data was prepared according to the </span><span>methodology</span><span> of SERENA soil erosion control cookbook</span><span> for the territory of </span><span>Slovakia</span><span>. </span><span>The map of soil loss by water erosion (soil threat</span><span>) </span><span>was based on the </span><span>RUSLE model.</span> <span>or the soil erosion control, the difference between the erosion map without vegetation (C-factor = 1) and the erosion map with vegetation was calculated.</span></span><span> </span></p> </div> </div> <div> <p>The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. </p> </div> <div> <p>To create the soil loss map we used theese data: </p> </div> <div> <p> R factor - we used data from 100 automatic rain stations on minute rainfall for about 10-year period (national dataset) </p> </div> <div> <p>K factor – we used the source proposed in the cookbook from ESDAC dataset: Soil Erodibility (K- Factor) High Resolution dataset for Europe </p> </div> <div> <p>LS factor – we used the source proposed in the cookbook from ESDAC dataset: LS-factor (Slope Length and Steepness factor) for Slovakia </p> </div> <div> <p>C factor – we used LPIS database-this has information about crops on agricultural soil. We have values of C factor for all crops. </p> </div> <div> <p>P factor – we used the source proposed in the cookbook from ESDAC dataset: P factor for Slovakia. This map has values about 0.99 for Slovakia, so P-factor does not have much effect on the resulting erosion. </p> </div> <div> <p>The delivered map was prepared in GeoTIFF format in the resolution of 500 * 500 m. </p> </div>
SERENA EJPSOIL PL EROSION CONTROL SOIL MASS NOT ERODED
<p>General description of SERENA</p> <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>Files description</p> <p>Data was prepared as a result of SERENA EJP SOIL. The attached files are a part of the analysis of Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3 (https://doi.org/10.5281/zenodo.13991087). The RUSLE method was used to prepare the attached files. </p>
SERENA EJPSOIL PL EROSION SOIL LOSS
<p>General description of SERENA</p> <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>Files description</p> <p>Data was prepared as a result of SERENA EJP SOIL. The attached files are a part of the analysis of Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3 (https://doi.org/10.5281/zenodo.13991087). The RUSLE method was used to prepare the attached files. All attached GeoTIFFs were described below:</p> <p>SERENA_EJPSOIL_PL_EROSION__K_factor_2018.tif</p> <p>The result of modelling K factor - soil-erodibility factor </p> <p>SERENA_EJPSOIL_PL_EROSION_C_factor_2018.tif</p> <p>The result of modelling C factor - land cover and management factor</p> <p>SERENA_EJPSOIL_PL_EROSION_LS_factor_2018.tif</p> <p>The result of modelling LS factor - slope length and steepness factor (Source: https://esdac.jrc.ec.europa.eu/themes/slope-length-and-steepness-factor-ls-factor)</p> <p>SERENA_EJPSOIL_PL_EROSION_P_factor_2018.tif</p> <p>The result of modelling P factor - support practices factor (Source: https://esdac.jrc.ec.europa.eu/themes/support-practices-factor)</p> <p>SERENA_EJPSOIL_PL_EROSION_R_factor_2018.tif</p> <p>The result of modelling R factor -erosivity factor (rainfall event's ability to cause soil water erosion)</p> <p>SERENA_EJPSOIL_PL_EROSION_SOIL_LOSS_2018.tif</p> <p>Total soil erosion loss by water modelled for agricultural soils in Poland for 2018 (Map unit: <span>Mg ha<sup>−1</sup> yr<sup>−1</sup></span>)</p> <p>SERENA_EJPSOIL_PL_EROSION_SOIL_LOSS_MAX_EROSION_2018.tif</p> <p>Total maximum soil erosion loss by water modelled for agricultural soils in Poland for 2018 (Excluding C factor) (Map unit: <span>Mg ha<sup>−1</sup> yr<sup>−1</sup></span>)</p>
Flume Erosion Testing Data of Root-Permeated and Organic Matter Amended Soil Samples Using Three Streambank Boundary Conditions.
The data published here is expected to accompany one publicly available dissertation (Chapter 6 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Artificial Roots and Soil Microorganisms Increase Soil Resistance to Fluvial Erosion
Daily rainfall series and rainfall erosivity in Mexico for three climatic normals (1968-1997, 1978-2007, and 1988-2017)
As in many countries around the world, there are some issues in the Mexican rainfall series, such as missing values, short measurement periods, and series homogeneity (breaks due to station relocation and measurement mistakes), which further compound the challenge of using climate data. Furthermore, it is necessary to develop a complete and helpful rainfall series database by following an imputing and homogenization process of the rainfall series. This research has compiled and systematized a national dataset with daily rainfall and rainfall erosivity for three climatic normals CN (1968-1997, 1978-2007, and 1988-2017). We have used the "climatol" package to fill the data. After, we calculated daily rainfall erosivity using a power law model. As a result, we obtained 1370, 1679, and 1683 rainfall series for the CNs 1968-1997, 1978-2007, and 1988-2017, respectively. The median values of the rainfall erosivity for the three CNs were 3245, 3070, and 3327 MJ mm/ ha h yr, respectively. We are making this database available for public consultation for researchers and students, technical assistants, decision-makers, and others interested in environmental studies in Mexico.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Undisturbed tussock tundra
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 100 years of undisturbed tussock tundra. Data is presented for day 250 of each year.
Erosion Rates, Soil Core Descriptions and Organic Matter on the Virginia Coast
These data include stratigraphic, organic matter, and organic carbon analyses of sediment cores, as well as values used to calculate the time-averaged carbon erosion rate for the central 10 islands of the Virginia Barrier Island chain.
Hydrodynamic field data near Galveston, Texas wetland edges to help assess storm impacts and erosion
<p>Water free surface elevation measurements via submerged pressure transducers along transects near Galveston Bay wetland edges</p>
Modelling NBSs for coastal erosion and marine flooding: the Emilia-Romagna case studies
<p>The study was conducted in the context of the OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks (OPERANDUM) project which is an H2020 project which aims at providing tools and methodologies for the assessment of NBS efficiency around the world. Two NBs were tested via modelling simulations on the Bellocchio Beach at Lido di Spina (Italy) located in the northern part of the Emilia-Romagna coast (northern Adriatic Sea): an artificial dune built with natural materials and a marine seagrass meadow.</p> <p>The artificial dune is an engineered structure that will mimic the functioning of natural dunes. Its aims are reducing both natural dune erosion and flooding in adjacent coastal lowlands. It consists of a barrier between the sea and land, in a similar way to a seawall. Unlike the latter, the NBS are ‘dynamic’, i.e. the dune/beach system interacts a great deal and is constantly undergoing small adjustments in response to changes in wind and wave climate or sea level. Its construction involves the placement of sediment from dredged sources on the beach and it will be reinforced with a structure composed of biodegradable material. Different typologies of experimental solutions are foreseen.</p> <p>The second NBS consists of an alongshore seagrass belt located in front of the coastal area. It was investigated as a potential mechanism for wave amplitude reduction. Among the few species that can live in the northern Adriatic Sea, Zostera Marina was chosen due to its ability to live in a marine environment influenced by freshwaters. A more detailed description can be found in (Pillai et al., 2021).</p> <p>The numerical model chain, specifically developed for the study, consists of an Ocean Circulation model, so-called SHYFEM (Umgiesser et al., 2004), a wave model, so-called WWIII (Alves and Ardhuin, 2016), and a morphological model, so-called XBeach (Roelvink et al., 2009). Ten years of XBeach simulations have been executed to simulate the morphological impacts on the coastal strip for the present (2010-19) and future climate (2040-49). For each 10 years period, four scenarios were simulated: the baseline scenario without NBS (baseline_run), the scenario with the dune (dune_run), the scenario with the seagrass effect (seagrass_run) and the scenario with the two NBS integration (dune_seagrass_run).XBeach was forced with sea level and wave time series predicted by the SHYFEM and WWIII models respectively.</p> <p>The model domain consists in a curvilinear structured grid of about 3.2 km (longshore) x 2.8 km (cross-shore) covering the coastal stretch of Bellocchio beach at Lido di Spina (Italy) and extends seaward up to about 10 m depth.</p> <p>The performance of the NBSs and their impact on coastal erosion and marine flooding were investigated. For both present and future scenarios (201-2019 and 2040-2049), the reduction in wave intensity obtained with the seagrass provided greater benefits in terms of erosion mitigation and flood reduction. The analysis highlighted the limited scale of the dune intervention, in particular under present conditions, highlighting that the longer the artificial dune implemented, the larger the beach and dune area protected. For the future scenarios, the results are still significant and even small projects are expected to help in mitigating coastal erosion and marine flooding.</p> <p>For long-period simulations, no relevant improvements in reducing beach erosion was observed when the artificial dune was combined with the seagrass meadows with respect to the seagrass effects only. Instead, a dominant increase in sea levels will probably highlight the dune functions in hindering the marine ingression into the lagoon area behind and the consequent sediment redistribution.</p> <p>This dataset consists of XBeach model results, mainly:</p> <ul> <li>Morphological evolution of the coastal bottom at Bellocchio beach (Lido di Spina, Italy) in terms of initial and final bed levels, for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf)</li> <li>Maximum flood depth, defined as the non-simultaneous maximum water depth on the beach domain of Bellocchio (Lido di Spina, Italy) for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf) </li> <li>Erosion-deposition maps for the current (201-2019) and the future (2040-2049) scenarios. Results are available for the four NBS scenarios described above (and detailed in the Presentation.pdf).</li> </ul>
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>
SERENA EJPSOIL NL EROSION SOILLOSS
<p><span><span>The internal EJP SOIL project </span></span><span><span>SERENA contributed to the evaluation of soil multifunctionality aiming </span></span><span><span>at providing assessment tools for land planning and soil policies at different scales. By co-working </span></span><span><span>with relevant stakeholders, the project provided co-developed indicators and associated cookbooks </span></span><span><span>to assess and map them, to report both on soil degradation, soil-based ecosystem services and </span></span><span><span>their bundles, under actual conditions and for climate and land-use changes, at the regional, </span></span><span><span>national, and European scales.</span></span></p> <p><span>The dataset corresponds to a map of potential soil loss (Mg/ha/yr) due to erosion risk. The map is the result of applying the Erosion cookbook developed in SERENA/EJP-Soil. The map is created by calculating the erosion factor by the RUSLE model and has 25 m spatial resolution.</span></p>
SERENA EJPSoil Soil erosion control in Tuscany (Italy)
<div> <p><span><span>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.</span></span></p> <p><span><span>One of the </span><span>objective</span><span> 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 </span><span>methodology</span><span> of the SERENA Soil erosion and soil erosion control cookbook</span><span>. </span><span>Soil loss was used as an indicator for soil erosion (ST). T</span><span>he map of soil mass not eroded was based on the RUSLE model. </span><span>Soil erosion control was calculated as the difference between potential and actual soil erosion (SES, ecosystem service of soil erosion protection, i.e. soil eroded mass </span><span>retained</span><span> by vegetation, Mg/ha/y)</span><span>. For Italy, the cookbook was applied in the Tuscany region. </span></span><span> </span></p> </div> <div> <p><span><span>To create the soil loss map we used:</span></span><span> </span></p> </div> <div> <ul> <li><span><span>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 </span><span>Consortium) and</span><span> a local linear equation between R and mean annual precipitation (P);</span></span> </li> <li><span>for C -factor, Regional Land use map 1:10.000 (2018, freely available at: </span><span>https://www502.regione.toscana.it/geoscopio/usocoperturasuolo.html</span><span>) and ESDAC method (</span><span>https://doi.org/10.1016/j.landusepol.2015.05.021</span><span>) ;</span> <span> </span></li> <li><span>for K-factor, sand, silt, clay, and O.C. (%) maps (built from 4.000 soil profiles, following FAO’s </span><span>methodology</span><span> in GSP-GSOC map, Lamma Consortium), and Torri et al. (1997) function;</span><span> </span></li> <li><span>for LS-factor, DEM 10 m of Tuscany, (freely available at </span><span>https://www502.regione.toscana.it/geoscopio/cartoteca.html99</span><span>) and Desmet & </span><span>Govers</span><span> (1996) SAGA tool (applied at 10 m and upscaled);</span><span> </span></li> <li><span>for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor </span><span>of 0.5</span><span> was considered, based on expert evaluation)</span><span> </span></li> <li><span>for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor </span><span>of 0.5</span><span> was considered, based on expert evaluation)</span><span> </span></li> </ul> </div> <div> <p><span><span>Maps was delivered in the </span><span>GeoTIFF</span><span> format in the resolution of 100m. </span></span><span> </span></p> </div> <div> <p><span><span>Delivered data will be </span><span>validated</span><span> by stakeholders from Italy (scientist) in </span><span>October,</span><span> 2024.</span></span><span> </span></p> </div>
Dataset used for the analysis described in "Spatial patterns and controls on wind erosion in the Great Basin"
<p>This data set contains AERO model outputs and associated Bureau of Land Management Assessment, Inventory, and Monitoring calculated values for functional plant group cover estimates for monitoring plots across the Great Basin. Versrion 2 (V2) includes MLRA number and sampling year column ("sample_yr") that were omitted in previous version.</p>
Flume Erosion Testing of Unamended and Organic Matter Amended Soil Samples Using an Acoustic Doppler Profiler, 2021
This data accompanies a publication titled "Soil Amended with Organic Matter Increases Fluvial Erosion Resistance of Cohesive Streambank Soil". Briefly, fluvial erosion testing was conducted on soil samples using an indoor flume channel. Soil samples were previously collected from the riparian zone of a river near Virginia Tech's campus in Blacksburg, VA, USA. The soil was subsequently air-dried and stored until use. Prior to erosion testing, soil samples were amended with varying amounts of organic matter (0%, 1%, and 4% OM by mass), compacted to a bulk density of 0.95 KilogramsPerCubicCentiMeters in growth containers, and allowed to mature in a greenhouse setting for 50 days prior to flume erosion testing. An Acoustic Doppler Profiler (ADP) was used to measure soil erosion and collect three-dimensional velocity data during erosion tests; raw velocity and soil depth data for each sample tested were stored in MATLAB files. Follow testing, the soil remaining from each sample was collected, stored, and analyzed for aggregate stability, soil organic matter (SOM), and extracellular polymeric substances (EPS). Additionally, soil temperature, water temperature, and volumetric water content were also measured prior to or during erosion testing. Data collected from this study, and the accompanying ADP MATLAB files, are presented here.
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