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1,200 results for “Sierra”
Uplift and Seismicity driven by Magmatic Inflation at Sierra Negra Volcano, Galápagos Islands
<p>Catalogue of detected earthquakes and cGPS uplift timeseries for Sierra Negra Volcano, Galapagos Islands</p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Sierra Leone
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2025)</li> <li>railways (OpenStreetMap, 2025)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2025) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Dataset of processed Sentinel-2 images for chlorophyll-a estimation in high-altitude lakes in the Sierra Nevada, Spain
<p>This dataset contains Sentinel 2 satellite images clipped to 5 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. The images were processed with the following atmospheric correction algorithms:</p><ul><li><a href="https://c2rcc.org/">C2RCC</a> (<a href="https://ui.adsabs.harvard.edu/abs/2016ESASP.740E..54B/abstract">Brockmann et al. 2016</a>)</li><li><a href="https://github.com/MarcYin/SIAC">SIAC</a> (<a href=" https://doi.org/10.5194/gmd-15-7933-2022">Yin et al. 2022)</a></li><li><a href="https://github.com/acolite/acolite/releases/tag/20221114.0">ACOLITE</a> (<a href="https://doi.org/10.1016/j.rse.2018.07.015">Vanhellemont & Ruddick, 2018</a>)</li><li><a href="https://grass.osgeo.org/grass83/manuals/i.atcorr.html">6SV</a> (<a href="https://doi.org/10.1109/36.581987">Vermote et al. 2006</a>)</li></ul><p><strong>Included Lakes and and their IDs:</strong></p><ul><li>Laguna de la Caldera (ID = P-2)</li><li>Laguna-embalse de las Yeguas (ID = D-6)</li><li>Laguna de Río Seco (ID = P-8)</li><li>Laguna Larga (ID = G-7)</li><li>Laguna de la Mosca (ID = G-11)</li></ul>
Outputs of the WiMMed hydrological model for Sierra Nevada (Spain). Sept2015-Aug2022
<p>Ecosystem Services related to flood prevention, aquifer recharge and erosion prevention in SIERRA NEVADA (Spain) were quantified through the WiMMed hydrological model (Watershed Integrated Model in Mediterranean Environments; Herrero et al., 2014). WiMMed is a distributed and physically based model that combines hourly and daily meteorological data with soil hydro-physical properties and land use and land cover information to simulate water balance and flow circulation at basin scale (see Herrero et al. (2014) for details). </p><p>In this study we applied the WiMMed model considering the land use and land cover data for 2020 (according to SIPNA) and the meteorological data from Sept2015 to Aug2022 to evaluate the value of ecosystem services, following the work made by Moreno-Llorca et al. (2020). Specific parameters, expressing the influence of vegetation changes in the hydrological processes of the study area, were considered, namely on evapotranspiration, interception, infiltration, overland flow and soil erodibility. Aquifer recharge (mm/m2/year) was calculated as the total volume of water moving from the soil into the aquifer and becoming groundwater. For that, the model firstly interpolates the precipitation at the cell scale (Herrero et al., 2009), and then calculates rainfall/snowfall partition, reproduces the interception from the vegetation, calculates the snow accumulation and melting (Herrero et al., 2009), and separates surface runoff from infiltration on the ground surface. Vertical and horizontal soil water movement was reproduced by a two-layer soil approach, using Darcy-Buckingham law with Mualem-vanGenuchten parameterization (Muñoz Carpena and Ritter Rodriguez, 2005). Evapotranspiration extract water from soil using a parameterization based on potential evapotranspiration and soil water content (Herrero et al., 2014). Water percolating through the second layer of soil becomes aquifer recharge. Soil erosion prevention (T/ha/year) was calculated by considering the inverse of soil loss by water flow concentration (rill processes) and raindrop impacts (interrill processes). WiMMed uses the variation of different parameters that link soil loss, with changes in vegetation cover and land uses, as described in (Millares et al., 2019). Changes on soil erodibility were estimated from vertical distribution of root biomass, by adapting empirical models (e.g. Gale and Grigal, 1987; Jackson et al., 1996) to Mediterranean environments reported previously (Martinez- Fernandez et al., 1995). From these estimations, distributed soil erodibility was calculated from the empirical model proposed by Flanagan and Livingstone (1995). The calibration and validation of the WiMMed model in Sierra Nevada has been conducted through a series of studies that analysed each hydrological process in the area and designed and corrected each WiMMed module, pertaining to snow (Herrero et al., 2009), soil (Aguilar and Polo, 2011), baseflow (Millares, 2008; Millares et al., 2009), river flow (Pérez-Palazón et al., 2014), or soil loss and sediment transportation (Bergillos et al., 2016; Millares et al., 2020).</p><p><strong>INPUT DATA</strong></p><p>The input data used in the hydrological simulations were:</p><ul><li>Digital elevation model from national remote sensing program PNOA-LIDAR MDT02 and the topographic features calculated by WiMMed from the DEM: surface drainage system, river delineation, slope, aspect, sky view factor and horizon (sky obstruction in 8 directions).</li><li>Meteorological data from more than 50 weather stations in the area: hourly/daily rainfall (mm), hourly and daily temperature (oC), daily solar radiation (MJ/m2), average daily wind speed (m·s−1), average daily relative humidity (%), average daily barometric pressure (hPa).</li><li>Physico-chemical and hydraulic properties of the soil selected from the available spatial database performed by Rodríguez (2008), in which thematic maps were obtained for Andalusia at a 250-m resolution: hydraulic conductivity (mm·h−1), saturation and residual moisture values (mm·mm−1), air-entry matric potential (mm), retention parameter of the van Genuchten (dimensionless) and soil thickness (mm).</li><li>Land cover and land use information from SIPNA 2020.</li><li>Aquifer regions and information from hydrogeological atlas of Andalusia (ITGE-Junta de Andalucía, 1998; Castillo, 2008).</li></ul><p><strong>OUTPUT DATA</strong></p><p>The results contained in this database are raster files in UTM ETRS89 30S, with a spatial resolution of 30x30 meters, for the whole SIerra Nevada. The raster files are Esri-ASCII ArcGIS (.asc) grids with 3846 columns (X) and 2099 rows (Y). There are different time scales for each variable. The prefix of the file indicates this time scale, namely "Ano" for annual maps, "mes" for monthly maps and "Tot" for the whole simulation. The suffix indicates the variable of interest:</p><ul><li>Pre: Accumulated precipitation (solid + liquid) in mm</li><li>T_m: Mean temperature in ºC</li><li>P_n: Accumulated snowfall in mm</li><li>ErT: Accumulated total erosion (rill + interrill) in kg/m2</li><li>ET0: Accumulated potential evapotranspiration in mm</li><li>EvC: Accumulated real evaporation from canopy (intercepted precipitation) in mm</li><li>EvN:Accumulated real sublimation from snow in mm</li><li>EvS: Accumulated real evapotranspiration ration from soil in mm</li><li>Exp: Accumulated direct runoff in mm</li><li>Fus: Accumulated snowmelt in mm</li><li>HSol1: Instantaneous soil moisture in surface layer 1 (upper 25 cm) in mm</li><li>HSol2: Instantaneous soil moisture in deep layer 2 in mm</li><li>Inf: Accumulated infiltration from surface into soil in mm</li><li>Per: Accumulated aquifer recharge (from soil to groundwater) in mm</li><li>Qlat: Accumulated lateral flow (horizontal movement of water between cells) in mm</li><li>Tmn: Minimum temperature in ºC</li><li>Tmx: Maximum temperature in ºC</li></ul><p>There are also some other grid files (Tot_XXX.asc) related to the initial and final conditions of the state variables or internal conditions of the model.</p><p><strong>References</strong></p><p>Aguilar, C., Polo, M.J., 2011. Generating reference evapotranspiration surfaces from the Hargreaves equation at watershed scale. Hydrol. Earth Syst. Sci. 15, 2495–2508. doi: 10.5194/hess-15-2495-2011.</p><p>Bergillos, R.J., Rodríguez-Delgado, C., Millares, A., Ortega-Sánchez, M., Losada, M.A., 2016. Impact of river regulation on a Mediterranean delta: assessment of managed versus unmanaged scenarios. Water Resour. Res. 52 (7), 5132–5148.</p><p>Castillo, A. 2008. Manantiales de Andalucía. Agencia Andaluza del agua, Consejería de Medio Ambiente, Junta de Andalucía, Sevilla, 410 pp.</p><p>Herrero, J., Polo, M.J., Moñino, A., Losada, M.A., 2009. An energy balance snowmelt model in a Mediterranean site. J. Hydrol. 371 (1-4), 98–107.</p><p>Herrero, J., Millares, A., Aguilar, C., Egüen, M., Losada, M.A., 2014. Coupling spatial and time scales in the hydrological modelling of mediterranean regions: WiMMed, in: CUNY Academic Works. In: Presented at the International Conference on Hydroinformatics, p. 8. ITGE-Junta de Andalucía: Atlas Hidrogeológico de Andalucía. Madrid, 216 pp., ISBN: 84-7840-351-5, available at: http: //aguas.igme.es/igme/publica/libros1 HR/libro110/lib110.htm, last access: 18 March 2012, 1998</p><p>Millares, A., 2008. Integración del caudal base en un modelo distribuido de cuenca. Estudio de las aportaciones subterráneas en ríos de montaña. University of Granada.</p><p>Millares, A., Polo, M.J., Losada, M.A., 2009. The hydrological response of baseflow in fractured mountain areas. Hydrol. Earth Syst. Sci. 13 (1261–1271), 2009.</p><p>Millares, A., Díez-Minguito, M., Moñino, A., 2019. Evaluating gullying effects on modeling erosive responses at basin scale. Environ. Modell. Software 111, 61–71. Millares, A., Herrero, J., Bermúdez, M., Leiva, J.F., Cantalejo, M., 2020. Long-term modelling of soil loss and fluvial transport processes in a mountainous semi-arid basin, southern Spain, in: River Flow 2020 - Twentieth International Conference on Fluvial Hydraulic. Delf, Netherlands.</p><p>Moreno-Llorca, R., Vaz, A. S., Herrero, J., Millares, A., Bonet-García, F. J., & Alcaraz-Segura, D. 2020. Multi-scale evolution of ecosystem services' supply in Sierra Nevada (Spain): An assessment over the last half-century. <i>Ecosystem Services</i>, <i>46</i>, 101204.</p><p>Muñoz Carpena, R., Ritter Rodriguez, A., 2005. Hidrología Agroforestal. Mundiprensa.</p><p>Pérez-Palazón, M. J., Pimentel, R., Herrero, J., & Polo-Gómez, M. J. 2014. Analysis of snow spatial and temporary variability through the study of terrestrial photography in the Trevelez river valley. In <i>Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI</i> (Vol. 9239, pp. 358-368). SPIE.</p><p>Rodríguez, J. A. 2008. Sistema de Inferencia Espacial de Propiedades Físico-Químicas e Hidráulicas de los Suelos de Andalucía. Herramienta de Apoyo a la Simulación de Procesos Agro-Hidrológicos a Escala Regional. Informe Final. Empresa Pública Desarrollo Agrario y Pesquero, Consejería de Agricultura y Pesca, Sevilla.</p><p> </p><p> </p>
Litterbags from Sierra de Guadarrama
<p>Dataset associated to an experiment where we test the effects of initial mass of leaf litter on breakdown rate and invertebrate colonization.</p> <ul> <li>Stream: name of the stream where the litterbags were incubated. HC: Hoyo Claveles, SM: Santa María.</li> <li>Days: number of days of incubation.</li> <li>Ddays: number of degree-days of incubation.</li> <li>Initial: initial mass of leaf litter (3, 5 or 7 g).</li> <li>Size: meash-size of the sieve where the invertebrates were found. Big: 1 mm, Small: 0,5 mm.</li> <li>AFDM: ash free dry mash (g) of leaf litter.</li> <li>Number: A reference to identify the litterbags.</li> </ul> <p> </p>
Forest restoration and fuels reduction work: Different pathways for achieving success in the Sierra Nevada
Fire suppression and past selective logging of large trees have fundamentally changed frequent-fire adapted forests in California. The culmination of these changes produced forests that are vulnerable to catastrophic change by wildfire, drought, and bark beetles, with climate change exacerbating this vulnerability. Management options available to address this problem include mechanical treatments (Mech), prescribed fire (Fire), or combinations of these treatments (Mech + Fire). We quantify changes in forest structure and composition, fuel accumulation, modeled fire behavior, inter-tree competition, and economics from a 20-year forest restoration study in the northern Sierra Nevada. All three active treatments (Fire, Mech, Mech + Fire) produced forest conditions that were much more resistant to wildfire than the untreated control. The treatments that included prescribed fire (Fire, Mech + Fire) produced the lowest surface and duff fuel loads and the lowest modeled fire hazards. Mech produced low fire hazards beginning 7-years after the initial treatment and Mech + Fire had lower tree growth than controls. The only treatment that produced inter-tree competition similar to historical California mixed-conifer forests was Mech + Fire, indicating that stands under this treatment would likely be more resilient to enhanced forest stressors. While Fire reduced modeled fire hazard and reintroduced a fundamental ecosystem process, it was done at a net cost to the landowner. Using Mech that included mastication and commercial thinning resulted in positive revenues and was also relatively strong as an investment in reducing modeled fire hazard. The Mech + Fire treatment represents a compromise between the desire to sustain financial feasibility and the desire to reintroduce fire. One key component to long-term forest conservation will be continued treatments to maintain or improve the conditions from forest restoration. Many Indigenous people speak of 'active stewardship' as one of the key principles in land management and this aligns well with the need for increased restoration in western US forests. If we do not use the knowledge from 20+ years of forest research and the much longer tradition of Indigenous cultural practices and knowledge, frequent-fire forests will continue to be degraded and lost.
Rana sierrae annotated aquatic soundscapes (2022)
<p>This dataset is associated with the following manuscript, which contains details in the methodology of data collection and annotation: </p> <p>Lapp, S., Smith, T. C., Wilhelm, A, Knapp, R., Kitzes, J. In press. Aquatic soundscape recordings reveal diverse vocalizations and nocturnal activity of an endangered frog. The American Naturalist.</p> <p><em>Rana</em> <em>sierrae</em> (the Sierra Nevada yellow-legged frog) is an endangered species residing in high-elevation lakes in the Sierra Nevada mountains. The species is highly aquatic and, unlike most amphibians, primarily vocalizes while underwater. As a result, its vocalizations have rarely been recorded and its vocal repertoire is not well studied.</p> <p>This dataset contains an annotated set of underwater soundscape recordings containing <span>1236</span> annotations of <em>R. sierrae</em> vocalizations. We annotated five distinct vocalization types of<em> R. sierrae</em>, only two of which have been previously documented for this species. Besides the calls of <em>R.</em> <em>sierrae</em>, these audio recordings also contain stridulation sounds (not annotated), which were most likely produced by members of the family Corixidae or other aquatic invertebrates that stridulate underwater. </p>
Transport Starter Data Kit: Historical socio-transport data for Sierra Leone
<p>This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.</p>
Vulnerability tools - Sierra Morena (Spain)
<p><span>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Sierra Morena Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</span></p>
Socio- and Techno-Economic Dataset for Energy Modelling in Sierra Leone
<p>This repositary contains a Reference Energy Syatem (RES) and dataset containing the raw data used in the Sierra Leone energy models created by CCG and the Ministry of Energy in Sierra Leone including scenario-specific constraints used in the modelling. The models used were MAED and OSeMOSYS. Full information regarding data sources and assumptions used can be found in the corresponding Data in Brief.</p> <p>This work was supported by the Climate Compatible Growth Programme (#CCG) of the UK's Foreign Development and Commonwealth Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government's official policies.</p>
National Checklists: Sierra Leone Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Lám. 27.–Dictamnus hispanicus, a-g) Poveda de la Obispalía, Cuenca (MA 614840); h, i) Pico Martés, Valencia (MA 410644); j-n) Sierra de Salinas, Villena, Alicante (MA 370298): a) hábito; b) flor; c) pétalo, cara abaxial; d) pétalo, detalle del margen; e) pétalo, detalle de la uña; f) ápice del estambre; g) base del estambre; h) fruto inmaduro; i) fruto, detalle del indumento; j) fruto; k) fo- lículo, corte longitudinal; l) mitad del endocarpo; m, n) semillas. in Rhamnaceae- Polygalaceae
Lám. 27.–Dictamnus hispanicus, a-g) Poveda de la Obispalía, Cuenca (MA 614840); h, i) Pico Martés, Valencia (MA 410644); j-n) Sierra de Salinas, Villena, Alicante (MA 370298): a) hábito; b) flor; c) pétalo, cara abaxial; d) pétalo, detalle del margen; e) pétalo, detalle de la uña; f) ápice del estambre; g) base del estambre; h) fruto inmaduro; i) fruto, detalle del indumento; j) fruto; k) fo- lículo, corte longitudinal; l) mitad del endocarpo; m, n) semillas.
Fatiando a Terra Data: Sierra Negra volcano, Ecuador - Topography
<p>This is a topography point cloud of the 2018 lava flows of the Sierra Negra volcano, located on the Galápagos islands, Ecuador. The data are generated using structure from motion (SFM) and shows nice topographic features and different roughness of the lava flows. Good to show examples of calculating slope and other terrain properties from the point cloud or gridded data.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Data were cropped to smaller region to align with previously published studies of the data and make file sizes under 10 Mb. Coordinates converted from UTM to WGS84 geographic. Export to a compressed CSV for easier loading with Pandas.</p> <p><strong>Source: </strong>Carr, B. (2020). Sierra Negra Volcano (TIR Flight 3): Galápagos, Ecuador, October 22 2018. Distributed by OpenTopography. <a href="https://doi.org/10.5069/G957196P">https://doi.org/10.5069/G957196P</a></p> <p><strong>Additional reference:</strong> Carr, B. B., Lev, E., Sawi, T., Bennett, K. A., Edwards, C. S., Soule, S. A., et al. (2021). Mapping and classification of volcanic deposits using multi-sensor unoccupied aerial systems. Remote Sensing of Environment. <a href="https://doi.org/10.1016/j.rse.2021.112581">https://doi.org/10.1016/j.rse.2021.112581</a></p> <p><strong>Source license: </strong><a href="https://doi.org/10.5069/G957196P">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/sierra-negra-topography">https://github.com/fatiando-data/sierra-negra-topography</a></p>
APPENDIX 2 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape
APPENDIX 2. — Distribution maps of all the Cholevinae species collected in the MSS of the Sierra de Guadarrama National Park, except for Choleva (Cholevopsis) punctata Brisout, 1866.
FIG. 6 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape
FIG. 6. — Relation between the pronotum width (PWmid) and the pronotum length (PL) of both sexes of Choleva (Cholevopsis) punctata Brisout, 1866.
FIG. 3 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape
FIG. 3. — Distribution of Choleva (Cholevopsis) punctata Brisout, 1866 in the MSS of the Sierra de Guadarrama National Park. Legends and symbols: ● subterranean sampling devices (SSDs); Δ, talus pitfall traps (TSP); ● and presence of C. (C.) punctata. The combination of the different manifestations of the aedeagus with the different morphologies of the metatrochanter is shown for each SSD following the classification of Figs 6; 7.
FIG. 9 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape
FIG. 9. — Female genitalia of Choleva (Cholevopsis) punctata Brisout, 1866: A, left lateral vision; B dorsal vision without IX ltg and IX mtg; C, dorsal vision with complete genital shield; D, ventral vision; E, detail of the female genital armor. Scale bars: A, D, 0.5 mm; E, 0.2 mm. Abbreviations: see Material and methods. The abbreviations associated with the genital shield are those used by Deuve (1993).
FIG. 8 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape
FIG. 8. — The morphological diversity of the Choleva (Cholevopsis) punctata Brisout, 1866 metatrochanter from Sierra de Guadarrama: A-F, external spiny angle (hollow arrow with continuous contour), inner spiny angle (hollow arrow with discontinuous contour), and medial spine (solid arrow). Scale bar: 1 mm.
FIG. 7 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape
FIG. 7. — Different states of evagination of the inner sac with respect to the median lobe of the aedeagus of Choleva (Cholevopsis) punctata Brisout, 1866. Categorized in columns (I-IV states) and in rows. Abbreviations: lat, lateral view; v, ventral view. Scale bars: 1 mm.
FIG. 1 in Cholevinae (Coleoptera: Leiodidae) of the Sierra de Guadarrama National Park, Spain: occurrence in the MSS of a siliceous landscape
FIG. 1. — Locations of the 33 scree slopes and four talus that were sampled in the Sierra de Guadarrama National Park and in the surrounding area. Symbols: ●, subterranean sampling devices (SSDs); ∆, talus pitfall traps (TSPs).
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