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476 results for “EROSION”
Erosion and biomass measurements from microcosm experiments, 2022
This dataset accompanies the manuscript: "Microalgae and meiofauna induce heterogeneous biostabilization in marine sediments". It includes erosion and biomass measurements from microcosm experiments, along with R and MATLAB scripts to reproduce the analyses and figures. Initial diatom cultures were collected from Dauphin Island Alabama during the spring of 2022. All experiments were conducted at the Navel Research lab in Stennis Space Center, Stennis MS. in the summer of 2022.
REDB-BR: Rainfall Erosivity Database for Brazil
<p>This is REDB-BR, the Rainfall Erosivity Database for Brazil from the MSWEP rainfall dataset.</p> <p>It provides the R factor from the Universal Soil Loss Equation (USLE) in a 0.1º resolution grid, developed with 37 years of rainfall data from the MSWEP dataset.</p> <p>The R factor was calculated trough 73 erosivity index regression equations, which mostly uses a relation between monthly precipitation and annual precipitation, the Modified Fournier Index (MFI), and represents a good approximation to locals with no sub-hourly data for long periods. </p> <p>The main product of REDB-BR is the R factor map, available also as a .tif raster. The database also includes the equations shapefile, Thiessen Polygons shapefile and the equations table. </p>
Modelling impacts of tramlines on soil erosion processes at the catchment scale.
<p>The data refers to the following article:</p> <p>Saggau, P., M. Kuhwald, W. B. Hamer, R. Duttmann (2021): Are compacted tramlines underestimated features in soil erosion modelling? A catchment‐scale analysis using a process‐based soil erosion model. In: Land Degradtaion & Development. doi: 10.1002/ldr.4161</p> <p>The data only contains distributable raw data and R-codes.</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>
Data associated with the Tectonics manuscript "Building a Young Mountain Range: Insight into the Growth of the Greater Caucasus Mountains from Detrital Zircon (U-Th)/He Thermochronology and 10Be Erosion Rates"
<p>U-Pb and U-Th/He ages of zircons from a suite of detrital catchments reported in the manuscript "Building a Young Mountain Range: Insight into the Growth of the Greater Caucasus Mountains from Detrital Zircon (U-Th)/He Thermochronology and 10Be Erosion Rates" submitted to Tectonics. Repository includes sample locations and DEMs of each sampled catchment.</p>
Annual rain erosion (R) in Brazil
<p>The erosivity data in Brazil. It has a spatial resolution of <strong>30 seconds (~ 1 km²)</strong>. The data set grid is in <strong>GeoTIFF</strong> <strong>format </strong>and corresponds perfectly to WorldClim. It uses the <strong>geographic coordinate</strong> reference system, with <strong>WGS84 projection (EPSG: 4326)</strong>.</p> <p>Soil is a most important non-renewable natural resource for sustaining life. The rates of soil loss have been increasing. The strength of storms can become a disturbing factor, this water energy is known as rain erosivity, and is a major cause of the loss of sediment and nutrients worldwide. The method of obtaining these values is not simple and is usually one-off and uses the USLE or RUSLE equation. Point values cannot be applied in areas that need to estimate soil losses. And traditional spatialization techniques like kriging, IDW or Thiessen polygons do not represent the variability that actually occurs. Thus, the objective of this article was to model a map of rainfall erosivity for Brazil, with spatial resolution of 30 seconds of arc (~ 1 km²). Using products made available by other articles, GIS techniques and machine learning modeling. Of the 31 pre-selected covariates 8 were used in the modeling, in order of importance, they were: Longitude, Solar Radiation, Annual precipitation (BIO12), Precipitation of the coldest quarter (BIO19), Wind speed, Precipitation of the warmest quarter (BIO18 ) and the annual reference evapotranspiration. After 400 trainings and validations, the model with the best performance indicators was the Random Forest, using the medians, the indices were: NSE of 0.5823, RMSE of 1567.17 MJ.mm/ha.h.ano, MAE of 1135.90 MJ.mm / ha.h.year, nRMSE of 58.50%, ME of -17.76 MJ.mm/ha.h.year and D of 0.8487.</p> <p>The article was submitted for publication.</p> <p>Dados_Erosividade_BR.csv - Data used to model the models.<br> eros_cubist.tif - Erosivity image generated by the cubist model<br> eros_gbm.tif - Image of erosivity generated by the gbm model<br> eros_lm.tif - Erosivity image generated by the linear model<br> eros_rf.tif - Erosivity image generated by the random forest model</p>
Riverbank Erosion and Accretion from Landsat
<p>Riverbank erosion presents a serious risk to people and infrastructure. These risks are becoming increasingly hard to predict because of direct modification of the rivers by damming and bank stabilization, as well as indirect modification by climate change and land use change. Geomorphologists have developed scaling relationships based on watershed characteristics; however, these relationships are very coarse and limited by the available data. This research uses twenty years of satellite imagery to develop the first global dataset of riverbank erosion, which both confirms existing knowledge and opens new avenues of research.</p>
GloRESatE - Global Rainfall Erosivity from Reanalysis and Satellite Estimates
<p>Rainfall erosivity measures the impact of rainfall kinetic energy and intensity or its potential to cause soil erosion. The sparsely available gauge rainfall dataset limits reliable rainfall erosivity assessment globally. GloRESatE is a state-of-the-art global rainfall erosivity dataset with a high spatial resolution of 0.1° × 0.1°. It integrates satellite data (CMORPH, IMERG Final Run), reanalysis data (ERA5-Land), and observations from 6,170 gauge stations worldwide. Created using advanced Gaussian Process Regression, this dataset provides accurate and reliable rainfall erosivity information. It serves as a vital resource for hydrological research, aiding studies in soil erosion, water resource management, and climate change impact assessments on a global scale.</p> <p> </p> <p>Das, S., Jain, M.K., Gupta, V., McGehee, R.P., Yin, S., de Mello, C.R., Azari, M., Borrelli, P. and Panagos, P., 2024. GloRESatE: A dataset for global rainfall erosivity derived from multi-source data. <em>Scientific Data</em>, <strong>11</strong>:926. https://doi.org/10.1038/s41597-024-03756-5</p>
Data from "Into the unknown: The role of post-fire soil erosion in the carbon cycle"
<p>Wildfires directly emit 2.1 Pg carbon (C) to the atmosphere annually. The net effect of wildfires on the C cycle, however, involves many interacting source and sink processes beyond these emissions from combustion. Among those, the role of post-fire enhanced soil organic carbon (SOC) erosion as a C sink mechanism remains essentially unquantified. Wildfires can greatly enhance soil erosion due to the loss of protective vegetation cover and changes to soil structure and wettability. Post-fire SOC erosion acts as a C sink when off-site burial and stabilization of C eroded after a fire, together with the on-site recovery of SOC content, exceed the C losses during its post-fire transport. Here we synthesize published data on post-fire SOC erosion and evaluate its overall potential to act as longer-term C sink. To explore its quantitative importance, we also model its magnitude at continental scale using the 2017 wildfire season in Europe. Our estimations show that the C sink ability of SOC water erosion during the first post-fire year could account for around 13% of the C emissions produced by wildland fires. This indicates that post-fire SOC erosion is a quantitatively important process in the overall C balance of fires, and highlights the need for more field data to further validate this initial assessment.</p> <p>Here we provide the post-fire SOC erosion dataset ("Post-fire SOC erosion rates" file) used for calculating the SOC ratio of eroded sediments implemented in the RUSLE modelling; as well as the list of data sources ("List of data sources" file).</p>
Alpine ice sheet erosion potential aggregated variables
<p>These data contain domain-integrated and time-integrated model output variables presented in the reference below or otherwise relevant to last glacial cycle glacier erosion in the Alps.</p> <p><strong>Reference:</strong></p> <ul> <li>J. Seguinot and I. Delanay. Last glacial cycle glacier erosion potential in the Alps, <em>submitted to Earth Surface Dynamics Discussions</em>, 2021.</li> </ul> <p><strong>File names:</strong></p> <pre><code>alpero.{1km|2km}.{epic|grip|md01}.{cp|pp}.agg.nc</code></pre> <ul> <li>Horizontal resolution: <ul> <li><em>1km</em>: 1 km horizontal resolution</li> <li><em>2km</em>: 2 km horizontal resolution</li> </ul> </li> <li>Temperature forcing: <ul> <li><em>epic</em>: EPICA ice core temperature forcing</li> <li><em>grip</em>: GRIP ice core temperature forcing</li> <li><em>md01</em>: MD01-2444 core temperature forcing</li> </ul> </li> <li>Precipitation forcing: <ul> <li><em>cp</em>: constant precipitation</li> <li><em>pp</em>: palaeo-precipitation reduction</li> </ul> </li> </ul> <p><strong>Variables:</strong></p> <ul> <li>Coordinate variables: <ul> <li><em>x</em>: X-coordinate in Cartesian system</li> <li><em>y</em>: Y-coordinate in Cartesian system</li> <li><em>lon</em>: longitude</li> <li><em>lat</em>: latitude</li> <li><em>time</em>: time</li> <li><em>age</em>: model age</li> <li><em>z</em>: elevation band midpoints</li> <li><em>d</em>: distance along transect</li> </ul> </li> <li>Glacier erosion variables: <ul> <li><em>coo2020_cumu</em>: Cook et al. (2020) cumulative glacial erosion potential</li> <li><em>coo2020_rate</em>: Cook et al. (2020) domain total volumic erosion rate</li> <li><em>coo2020_hyps</em>: Cook et al. (2020) erosion rate geometric mean</li> <li><em>coo2020_rhin</em>: Cook et al. (2020) rhine transect erosion rate</li> <li><em>her2015_cumu</em>: Herman et al. (2015) cumulative glacial erosion potential</li> <li><em>her2015_rate</em>: Herman et al. (2015) domain total volumic erosion rate</li> <li><em>her2015_hyps</em>: Herman et al. (2015) erosion rate geometric mean</li> <li><em>her2015_rhin</em>: Herman et al. (2015) rhine transect erosion rate</li> <li><em>hum1994_cumu</em>: Humphrey and Raymond (1994) cumulative glacial erosion potential</li> <li><em>hum1994_rate</em>: Humphrey and Raymond (1994) domain total volumic erosion rate</li> <li><em>hum1994_hyps</em>: Humphrey and Raymond (1994) erosion rate geometric mean</li> <li><em>hum1994_rhin</em>: Humphrey and Raymond (1994) rhine transect erosion rate</li> <li><em>kop2015_cumu</em>: Koppes et al. (2015) cumulative glacial erosion potential</li> <li><em>kop2015_rate</em>: Koppes et al. (2015) domain total volumic erosion rate</li> <li><em>kop2015_hyps</em>: Koppes et al. (2015) erosion rate geometric mean</li> <li><em>kop2015_rhin</em>: Koppes et al. (2015) rhine transect erosion rate</li> </ul> </li> <li>Other variables: <ul> <li><em>cumu_sliding</em>: cumulative basal motion</li> <li><em>glacier_time</em>: total ice cover duration</li> <li><em>warmbed_time</em>: temperate-based ice cover duration</li> <li><em>glacier_area</em>: glacierized area</li> <li><em>volumic_lift</em>: volumic bedrock uplift</li> <li><em>warmbed_area</em>: temperate-based ice cover area</li> </ul> </li> </ul> <p><strong>Data format:</strong></p> <p>The data use compressed netCDF format. For quick inspection I recommend ncview. Conversion to GeoTIFF (and other GIS formats) can be achieved with e.g. GDAL::</p> <pre><code>gdal_translate NETCDF:filename.nc:variable filename.variable.tif</code></pre> <p>The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. To convert all variables to separate files use:</p> <pre><code>gdalinfo $filename | grep NETCDF | cut -d '=' -f 2 | egrep -v '(lat|lon|time_bounds)' | while read sub do gdal_translate $sub ${filename%.nc}.${sub##*:}.tif done</code></pre> <p>Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata. Also see glacial cycle <a href="https://doi.org/10.5281/zenodo.1423160">aggregated</a> and <a href="https://doi.org/10.5281/zenodo.1423175">continuous</a> variables.</p> <p><strong>Changes:</strong></p> <ul> <li>Version 2: <ul> <li>Add variable for glacierized area within 100-m elevation band.</li> <li>Use 100-m instead of 10-m elevation bands for erosion rate.</li> </ul> </li> <li>Version 1: <ul> <li>Initial version.</li> </ul> </li> </ul>
Monitoring NBS for coastal erosion and marine flooding: the Emilia-Romagna case study
<p>The study was conducted in the context of the OPERANDUM project which is an H2020 project which aims at providing tools and methodologies for the assessment of NBS efficiency around the world. As NBS will be tested an artificial dune built with natural materials. </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 Bellocchio Beach at Lido di Spina (Italy) was initially chosen for the study, however the Volano beach was selected as the new study area because of the strong erosion caused by an intense storm event in December 2020 at Bellocchio. The dune was built on the Volano beach and monitoring surveys were carried out on this new site. </p> <p>A morphological monitoring aimed to assess the beach evolution and the performance of the NBS were performed. Monitoring of morphology evolution of shoreline and inland area provide information about impact of the NBS on coastal erosion. Furthermore, the changes in the form of the work give information about the resistance of the NBS to wave attacks. Sedimentological campaigns have been planned in order to provide information regarding the texture of the sediments present in the area detected and possibly highlight changes after the construction of the dune.</p> <p>Three monitoring campaigns were carried out before, immediately after and six months later the construction of the dune (January, May and October 2022). All data were analysed to assess local coastal dynamics and NBS evolution. </p> <p>The monitoring consisted of: </p> <ul> <li> <p>topographic and bathymetric surveys (GNSS and multibeam/singlebeam echosounder) to generate DTMs of the entire area (10 m cell size); </p> </li> <li> <p>aerial photogrammetric surveys by UAV for the production of orthophotos and high resolutions DTMs of the emerged beach (1m cell size) and of the dune area (0.2 m cell size); </p> </li> <li> <p>sediment sampling and grain size analysis. </p> </li> </ul> <p>Surveys show that morphological and sedimentological changes are determined mostly by anthropic actions to the beach and seabed maintenance (artificial winter banks and Sacca di Goro channel). </p> <p>Regarding the dune area no significant changes in morphology were observed due to the limited period between the surveys. Appreciable signals were detected, such as the natural recolonization by pioneer plant species and the slight sand accumulation on the dune foot.</p> <p>This dataset consists of data related to monitoring activities. </p>
Experimental erosion of microbial diversity decreases soil CH4 consumption rates
<p>Biodiversity-ecosystem functioning (BEF) experiments have predominantly focused on communities of higher organisms, in particular plants, with comparably little known to date about the relevance of biodiversity for microbially-driven biogeochemical processes. Methanotrophic bacteria play a key role in Earth’s methane (CH<sub>4</sub>) cycle by removing atmospheric CH<sub>4</sub> and reducing emissions from methanogenesis in wetlands and landfills. Here, we used a dilution-to-extinction approach to simulate diversity loss in a methanotrophic landfill cover soil community. Replicate samples were diluted 10<sup>1</sup> to 10<sup>7</sup>-fold, and pre-incubated under a high CH<sub>4</sub> atmosphere for the microbial communities to recover to approximately equal size. Then, the samples were incubated for 86 days at constant or diurnally-cycling temperature. Our hypotheses were that (1) CH<sub>4</sub> consumption would decrease as methanotrophic diversity was lost, and that (2) this effect would be more pronounced under variable environmental conditions (here: variable temperature). We followed net CH<sub>4</sub> consumption by gas chromatography. Microbial community composition was determined four times by DNA extraction and sequencing of amplicons specific to methanotrophs and bacteria (pmoA and 16S gene fragments). We found that the richness of operational taxonomic units (OTU) of methanotrophic and non-methanotrophic bacteria decreased approximately linearly with <em>log</em>-dilution. CH<sub>4</sub> consumption decreased with the number of taxonomic units lost. This effect was independent of community size, which we determined by quantitative PCR, and consistent over the study period. The temperature treatment (constant vs. cycling temperature) did not affect any of these results. The diversity effects we found occurred in relatively diverse communities, challenging the notion of high functional redundancy mediating high resistance to diversity erosion in natural microbial systems. The effects we report resemble the ones for higher organisms, suggesting that BEF-relationships are universal across taxa and spatial scales.</p>
Supplementary files for the manuscript "Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand", submitted to JGR Earth Surface
<p>This repository contains supplementary files to the manuscript ""Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand" submitted to JGR: Earth Surface. It contains: </p> <p>- The Matlab script used to find the optimal distance-from-fault and elevation windows ("elevation_distance_window_optimization"), and 3 text files used for input in this script ("data_erates" contains the erosion rates, "data_elev" the number of pixels in each elevation bin, "data_distAF" the number of pixels in each distance-from-fault bin). </p> <p>- An Excel spreadsheet with the same information that the input text files contain, but specifiying the elevation or distance from fault bin values ("elevation and distance from fault with bins")</p> <p>- A shapefile of catchment outlines ("WSAcatch") for the catchments sampled for CRN denudation rates</p> <p>- Raw CRN data ("Table 2_new_CRN_data")</p> <p>- Excel spreadsheet with the compilation of themochronometric cooling ages used in the age2exhume code (van der Beek & Schildgen, 2023; <a href="https://doi.org/10.5281/zenodo.7341603">https://doi.org/10.5281/zenodo.7341603</a>).</p> <p>CRN data and catchment outlines will also be uploaded to the OCTOPUS database (<a href="https://octopusdata.org/">https://octopusdata.org/</a>) after manuscript acceptance.</p>
Amendments and seeding did not augment erosion control structure effectiveness in dry rangelands, 2021-2023
This study investigates the effectiveness of combining rock structures with organic amendments (wood mulch or compost) and native perennial grass seed addition to address erosion on rangelands. The study was conducted across five cattle ranches in New Mexico with 9-18 active head cuts studied at each ranch. Rock rundown structures were built at each headcut and a plot above each structure received an organic amendment treatment (compost, mulch, or control) and seed addition treatment (seeded or control).
Chimney Pole Marsh Erosion-Camera Images and Video 2009-2012
This data consists of a time series of image and video files showing erosion at the western edge of Chimney Pole Marsh, in Northampton Co. Virginia. Images depict the edge of a salt marsh as it erodes. Images and videos have a time stamp (YYYYmmdd_HHMMss) embedded in their file name and also in the upper left of the images themselves. All dates and times are in Eastern Standard Time. Images and videos are taken once ever 30 minutes at 25 and 55 minutes after the hour. Still image JPEG (.jpg) files are 1600x1200 pixels in size. Videos are encoded as MPEG-4 (.mp4) files with a resolution of 400x304 at 8.05 frames per second. They were collected by a 2 mega-pixel Vivotek IP7161 security camera attached to a post (approximately 3-m above the marsh surface). The camera was removed when the marsh was sufficiently eroded that the camera platform was imperiled. There are some gaps in the data caused by network and electrical problems.
Annual rainfall erosivity in Greece
<p>Estimated mean annual erosivity values over Greece in (Mj.mm/ha/h/y) using precipitation records that suffered from a significant volume of missing values. As an intermediate step the creation of monthly precipitation and erosivity density models was utilized.</p>
Data related to the manuscript "Functional relationships between critical erosion thresholds of fine reservoir sediments and their sedimentological characteristics"
<p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Data used in the publication "Functional relationships between critical erosion thresholds of fine reservoir sediments and their sedimentological characteristics", which is accepted by the Journal of Hydraulic Engineering and will be published in a forthcoming issue.</p> <p>Beckers, F., K. Koca, S. Haun, M. Noack, S. U. Gerbersdorf, and S. Wieprecht. Forthcoming. „Functional relationships between critical erosion thresholds of fine reservoir sediments and their sedimentological characteristics.” J. Hydraul. Eng. <a href="https://doi.org/10.1061/(ASCE)HY.1943-7900.00019864">https://doi.org/10.1061/(ASCE)HY.1943-7900.0001984</a></p> <p> </p> <p>The data consists of two files:</p> <ul> <li>GBS.txt contains the sediment data of the reservoir <em>Großer Brombachsee</em></li> <li>SBT.txt contains the sediment data of the reservoir <em>Schwarzenbachtalsperre</em></li> </ul> <p>Both files contain information on the sediment depth and erosion stability separated into τ<sub>c,0</sub> and τ<sub>c,S</sub>.</p> <p>Furthermore, both files contain a collection of physico-chemical sediment parameters, including bulk density, sediment composition (Clay, Silt, Sand), percentiles (d<sub>10</sub>, d<sub>50</sub>, d<sub>90</sub>),cation exchange capacity (CEC), and organic content (TOC).</p> <p>Additionally, the SBT data contains biological sediment parameters, including chlorophyll-a (CHL-a), and extracellular polymeric substances separated into proteins (EPS-p) and carbohydrates (EPS-c).<br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The data was collected within the transdisciplinary research project "CHARM - Challenges of Reservoir Management".</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------</p>
Data from: How density dependence, genetic erosion, and the extinction vortex impact evolutionary rescue
<p>Following severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called evolutionary rescue. Models of evolutionary rescue demonstrate that initial size, genetic variation, and degree of maladaptation influence population fates. However, many models feature populations that grow without negative density dependence or with constant genetic diversity despite precipitous population decline, assumptions likely to be violated in conservation settings. We examined the simultaneous influences of density-dependent growth and erosion of genetic diversity on populations adapting to novel environmental change using stochastic, individual-based simulations. Density dependence decreased the probability of rescue and increased the probability of extinction, especially in large and initially well-adapted populations that previously have been predicted to be at low risk. Increased extinction occurred shortly following environmental change, as populations under density dependence experienced more rapid decline and reached smaller sizes. Populations that experienced evolutionary rescue lost genetic diversity through drift and adaptation, particularly under density dependence. Populations that declined to extinction entered an extinction vortex, where small size increased drift, loss of genetic diversity, and the fixation of maladaptive alleles, hindered adaptation, and kept populations at small densities where they were vulnerable to extinction via demographic stochasticity.</p>
Victorian Coastal Monitoring Program: (1) VCMP Sites; (2) VCMP Coastal Erosion Indicators
<p>This dataset was produced by the Victorian Coastal Monitoring Program (VCMP), Department of Energy, Environment and Climate Action, Victoria State Government, Australia.</p><p><a href="https://www.marineandcoasts.vic.gov.au/marine-and-coastal-knowledge/victorian-coastal-monitoring-program">https://www.marineandcoasts.vic.gov.au/marine-and-coastal-knowledge/victorian-coastal-monitoring-program</a> </p><p><a href="https://www.marineandcoasts.vic.gov.au/__data/assets/pdf_file/0023/625352/VCMP_Erosion-Indicators_April_2023.pdf">https://www.marineandcoasts.vic.gov.au/__data/assets/pdf_file/0023/625352/VCMP_Erosion-Indicators_April_2023.pdf</a></p><p>[ADD LINK TO VCMP SITES METADATA REPORT - ONCE PUBLISHED TO VCMP WEBSITE]</p><p>Products include: </p><p>(1) Complete processed shorelines dataset ('VCMP Sites'), including drone and satellite data, with transects, shorelines, time series, and cross-sections for drone surveys</p><p>(2) Summary outputs ('VCMP Coastal Erosion Indicators'), including the Erosion Warning Indicator (<i>EWI</i>) summary statistics and Erosion Hotspot Detector (<i>EHD</i>) outputs. </p><p>Survey data in this repository were updated to the start of 2023. </p><p>Regularly updated outputs for all VCMP products, including pre-processed VCMP drone data (digital surface models and orthomosaics) can be accessed through the Victorian government (contact <a href="mailto:vcmp@delwp.vic.gov.au">vcmp@delwp.vic.gov.au</a>; <a href="https://www.marineandcoasts.vic.gov.au/marine-and-coastal-knowledge/victorian-coastal-monitoring-program">https://www.marineandcoasts.vic.gov.au/marine-and-coastal-knowledge/victorian-coastal-monitoring-program</a>). </p><p>'VCMP Sites' and 'VCMP Coastal Erosion Indicator' outputs and a wide array of marine and coastal data are viewable through the decision support portal CoastKit (<a href="https://mapshare.vic.gov.au/coastkit/">https://mapshare.vic.gov.au/coastkit/</a>). </p><p>Select outputs are also downloadable in spatial file format through DataShare Victoria (<a href="https://datashare.maps.vic.gov.au/search?q=vcmp">https://datashare.maps.vic.gov.au/search?q=vcmp</a>).</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>
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