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9 results for “variable velocity”
Climate change velocity metrics calculated for three climate variables across Finland
<p>This dataset contains files that show the climate change velocity metrics calculated for three climate variables across Finland. The climate velocities were used to study the magnitude of projected climatic changes in a nation-wide Natura 2000 protected area (PA) network (Heikkinen et al., 2020). Using fine-resolution climate data that describes the present-day and future topoclimates and their spatio-temporal variation, the study explored the rate of climatic changes in protected areas on an ecologically relevant, but yet poorly explored scale. The velocities for the three climate variables were developed in the following work, where in-depth description of the different steps in velocity metrics calculation and a number of visualisations of their spatial variation across Finland are provided:</p><p>Risto K. Heikkinen 1, Niko Leikola 1, Juha Aalto 2,3, Kaisu Aapala 1, Saija Kuusela 1, Miska Luoto 2 & Raimo Virkkala 1 2020: Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678. https://doi.org/10.1038/s41598-020-58638-8</p><p>1 Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland</p><p>2 Department of Geosciences and Geography, University of Helsinki, FI-00014, Helsinki, Finland</p><p>3 Finnish Meteorological Institute, FI-00101, Helsinki, Finland </p><p>The dataset includes GIS compatible geotiff files describing the nine spatial climate velocity surfaces calculated across the whole of Finland at 50 m × 50 m spatial resolution. These nine different velocity surfaces consist of velocity metric values measured for each 50-m grid cell separately for the three different climate variables and in relation to the three different future climate scenarios (RCP2.6, RCP4.5 and RCP8.5). The baseline climate data for the study were the monthly temperature and precipitation data averaged for the period from 1981 to 2010 modelled at a resolution of 50-m, based on which estimates for the annual temperature sum above 5 °C (growing degree days, GDD, °C), the mean January temperature (TJan, °C) and the annual climatic water balance (WAB, the difference between annual precipitation and potential evapotranspiration; mm) were calculated. Corresponding future climate surfaces were produced using an ensemble of 23 global climate models for the years 2070–2099 (Taylor et al. 2012) and the three RCPs. The data for the three climate variables for 1981–2010 and under the three RCPs will be made available in separately via METIS - FMI's Research Data repository service (Aalto et al., in prep.). </p><p>The climate velocity surfaces included in the present data repository were developed using climate-analog approach (Hamann et al. 2015; Batllori et al. 2017; Brito-Morales et al. 2018), whereby velocity metrics for the 50-m grid cells were measured based on the distance between climatically similar cells under the baseline and the future climates, calculated separately for the three climate variables. In Heikkinen et al. (2020), the spatial data for the Natura 2000 protected areas were used to assess their exposure to climate change. The full data on N2K areas can be downloaded from the following link: https://ckan.ymparisto.fi/dataset/%7BED80465E-135B-4391-AA8A-FE2038FB224D%7D. However, note that the N2K areas including multiple physically separate patches were treated as separate polygons in Heikkinen et al. (2020), and a minimum size requirement of 2 hectares were requested. Moreover, the digital elevation model (DEM) data for Finland (which were dissected to Natura 2000 polygons to examine their elevational variation and its relationships to topoclimatic variation) can be downloaded from the following link: https://ckan.ymparisto.fi/en/dataset/dem25_astergdem25. </p><p>The coordinate system for the climate velocity data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). Summary of the key settings and elements of the study are provided below. A detailed treatment is provided in Heikkinen et al. (2020).</p><p>Code to the files (four files per each velocity layer: *.tif, *.tfw. *.ovr and *.tif.aux.xml) in the dataset: </p><p>(a) Velocity of GDD with respect to RCP2.6 future climate (Fig 2a in Heikkinen et al. 2020). Name of the file: GDDRCP26.*</p><p>(b) Velocity of GDD with respect to RCP4.5 future climate (Fig. 2b in Heikkinen et al. 2020). Name of the file: GDDRCP45.*</p><p>(c) Velocity of GDD with respect to RCP8.5 future climate (Fig. 2c in Heikkinen et al. 2020). Name of the file: GDDRCP85.*</p><p>(d) Velocity of mean January temperature with respect to RCP2.6 future climate (Fig. 2d in Heikkinen et al. 2020). Name of the file: TJanRCP26.*</p><p>(e) Velocity of mean January temperature with respect to RCP4.5 future climate (Fig. 2e in Heikkinen et al. 2020). Name of the file: TJanRCP45.*</p><p>(f) Velocity of mean January temperature with respect to RCP8.5 future climate (Fig. 2f in Heikkinen et al. 2020). Name of the file: TJanRCP85.*</p><p>(g) Velocity of climatic water balance with respect to RCP2.6 future climate (Fig. 2g in Heikkinen et al. 2020). Name of the file: WABRCP26.*</p><p>(h) Velocity of climatic water balance with respect to RCP4.5 future climate (Fig. 2h in Heikkinen et al. 2020). Name of the file: WABRCP45.*</p><p>(i) Velocity of climatic water balance with respect to RCP8.5 future climate (Fig. 2i in Heikkinen et al. 2020). Name of the file: WABRCP85.*</p><p>Note that velocity surfaces e and f include disappearing climate conditions.</p><p><strong>Summary of the study:</strong></p><p>Climate velocity is a generic metric which provides useful information for climate-wise conservation planning to identify regions and protected areas where climate conditions are changing most rapidly, exposing them to high rates of climate displacement (Batllori et al. 2017), causing potential carry-over impacts to community structure and ecosystem functions (Ackerly et al. 2010). Climate velocity has been typically used to assess the climatic risks for species and their populations, but velocity metrics can also be used to identify protected areas which face overall difficulties in retaining ecological conditions that promote present-day biodiversity. </p><p>Earlier climate velocity assessments have focussed on the domains of the mesoclimate (resolutions of 1–100 km) or macroclimate (>100 km scales), and fine-grained (<100 m) local climatic conditions created by variation in topography ('topoclimate'; Ackerly et al. 2010; 2020) have largely been overlooked (Heikkinen et al. 2020). This omission may lead to biased exposure assessments especially in rugged terrain (Dobrowski et al. 2013; Franklin et al. 2013), as well as a limited ability to detect sites decoupled from the regional climate (Aalto et al. 2017; Lenoir et al. 2017). This study provided the first assessment of the climatic exposure risks across a national PA (Natura 2000) network based on very fine-grained velocities of three established drivers of high latitude biodiversity. </p><p>The produce fine-grain climate velocity measures, 50-m resolution monthly temperature and precipitation data averaged for 1981–2010 were first developed, and based on it, the three bioclimatic variables (growing degree days, mean January temperature and annual climatic water balance) were calculated for the whole study domain. In the next phase, similar future climate surfaces were produced based on data from an ensemble of 23 global climate models, extracted from the CMIP5 archives for the years 2070–2099 and the three RCP scenarios (RCP2.6, RCP4.5 and RCP8.5)26. In the final step, climate velocities for each the 50 x 50 m grid cells were measured using climate-analog velocity method (Hamann et al. 2015) and based on the distance between climatically similar cells under the baseline and future climates.</p><p>The results revealed notable spatial differences in the high velocity areas for the three bioclimatic variables, indicating contrasting exposure risks in protected areas situated in different areas. Moreover, comparisons of the 50-m baseline and future climate surfaces revealed a potential wholesale disappearance of current topoclimatic temperature conditions from almost all the studied PAs by the end of this century.</p><p><strong>Calculation of climate change velocity metrics for the three climate variables</strong></p><p>The overall process of calculation of climate velocities included three main steps. </p><p>(1) In the first step, we developed high-resolution monthly average temperature and precipitation data averaged over the years 1981–2010 and across the study domain at a spatial resolution of 50 × 50 m. This was done by building topoclimatic models based on climate data sourced from 313 meteorological stations (European Climate Assessment and Dataset [ECA&D]) (Klok et al. 2009). Our station network and modelling domain covered the whole of Finland with an additional 100 km buffer. However, it was also extended to cover large parts of northern Sweden and Norway for areas >66.5°N, as well as selected adjacent areas in Russia (for details see Heikkinen et al. 2020). This was done to capture the present-day climate spaces in Finland which are projected to move in the future beyond the country borders but have analogous climate areas in neighbouring areas; this was done to avoid developing a large number of velocity values deemed as infinite or unknown in the data for Finland. </p><p>The 50-m resolution average air temperature data were developed for the study domain using generalized additive modelling (GAM), as implemented in the R-package mgcv version 1.8–7 (R Development Core Team 2011; Wood 2011). In this modelling we utilised variables of geographical location (latitude and longitude, included as an anisotropic interaction), topography (elevation, potential incoming solar radiation, relative elevation) and water cover (sea and lake proximity), and subsequent leave-one-out cross-validation tests to assess model performance (for full process description, see Aalto et al. 2017; Heikkinen et al. 2020). The resulting topoclimate data effectively captured the physiographic effects of solar radiation and cold-air pooling.</p><p>To produce gridded precipitation data, we applied global kriging interpolation to the data from 343 rain gauges from the ECA&D dataset. The interpolation was carried out using information on geographical location, topography (elevation and eastness index) and proximity to the sea and R package gstat. The eastness index was obtained from a sine-transforming aspect raster surface calculated from a 50 m × 50 m digital elevation model to capture the effect of prevailing westerly winds on the accumulated precipitation on windward slopes. The gridding was first run at a resolution of 500 × 500 m, whereafter gridded precipitation values were bilinearly interpolated into the same 50 × 50 m resolution as the air temperature data. </p><p>Next, the three bioclimatic variables ((i) growing degree days (GDD, °C days) indicating the accumulated warmth during the growing season; (ii) mean January air temperature - TJan, °C; (iii) climatic water balance - WAB, mm) were calculated for each 50 x 50 grid cell from the high-resolution gridded 1981–2010 ('baseline') climate data. Earlier research has demonstrated the ecological relevance of these three complementary variables which provide estimations of winter cold, seasonal warmth and moisture availability (Sykes et al. 1996; Luoto et al. 2006; Huntley et al. 2007, 2008). </p><p>Following Carter et al. (1991), GDD was calculated as the effective temperature sum above the base temperature of 5 °C as follows:</p><p><i>GDD</i>5 = <i>∑ni (Ti - Tb), if Ti -Tb > 5</i></p><p>where Ti denotes the mean temperature at day i, Tb represents the base temperature, and n is the length of the summation period. However, because the daily air temperature data was not available, here the GDD was estimated using monthly data as in Araújo & Luoto (2007). The WAB is the difference between the total annual precipitation sum and the potential evapotranspiration (PET), which was estimated from the monthly air temperatures following Skov and Svenning (2004): </p><p><i>PET </i>= 58.93 × <i>Tabove </i>0°<i>C </i>/ 12</p><p>(2) In the second step we developed data on future climates by using the climate projections from the ensemble of 23 global climate models (GCMs), derived from the Coupled Model Intercomparison Project phase 5 archives (Taylor et al. 2012). From these archives, we processed to predicted averaged changes in mean temperature and precipitation with respect to the baseline 1981–2010 for the years 2070–2099, and the three RCP scenarios (cf. Moss et al. 2010). As the Coupled Model Intercomparison Project phase 5 climate scenario data represent coarse-scale resolution data, we converted it to match our fine-resolution baseline climate data by interpolation. For this, the climate model data depicting the predicted change in mean temperatures and precipitation with respect to the baseline climate were bilinearly interpolated to the 50 × 50 m grid system, and the change predicted by the GCMs was added to the spatially detailed baseline climate data. After this, the bioclimatic variables were recalculated for each RCP scenario to allow the calculation of climate change velocities across the whole country and the Natura 2000 protected areas.</p><p>(3) In the third step we developed climate change velocities for the three bioclimatic variables using the climate-analog approach (Hamann et al. 2015) where velocity is calculated by measuring the distance between present-day locations with certain climatic conditions and their future climate analogues, divided by the number of years between the two points in time. Thus, we calculated climate-analog velocities for the 50-m resolution grid climate data by measuring the distance between climatically similar grid cells for the present and future climates under RCP2.6, RCP4.5 and RCP8.5. </p><p>Prior the actual climate-analog velocity measurements, the climate variable surfaces were converted from continuous values into classified variable surfaces. For this, we defined the boundary values for the variable classes so that the climatically matching grid cells had their within-class ranges as small as possible but, at the same time, avoided artefactual extreme precision. After a set of pilot reclassifications, the following within-class ranges were applied: GDD, within-class range 50 °C with 51 categories; TJan, within-class range 0.5 °C with 60 categories; WAB, within-class range 50 mm with 55 categories. Next, using the reclassified present-day and future climate surfaces the search of the minimum distances between grid cells with similar present-day and future GDD/TJan/WAB climates were executed. The search was carried out using the ArcGIS software (Desktop 10.5.1.) by employing the Euclidean distance function. The minimum distances measured for each 50-m grid cell were divided by the difference between the mean points in the two time slices, 1981–2010 and 2070–2099. </p><p>The resulting 50-m resolution climate velocity surfaces for the three climate variables are provided in the zipped files included this data repository. In Heikkinen et al. (2020), these climate velocity data were employed in a series of subsequent analyses. For example, high-velocity areas ('velocity hotspots') of the three climate variables were visually compared with each other based on maps showing their 50-m resolution velocities across mainland Finland and the degree of overlap between the present-day range and projected future range of the three climate variables were investigated in each of the 5,068 Natura 2000 polygons included in the study.</p><p><strong>References</strong></p><p>Aalto, J., Riihimäki, H., Meineri, E., Hylander, K., Luoto, M., 2017. Revealing topoclimatic heterogeneity using meteorological station data. International Journal of Climatology 37, 544-556.</p><p>Ackerly, D.D., Loarie, S.R., Cornwell, W.K., Weiss, S.B., Hamilton, H., Branciforte, R., Kraft, N.J.B., 2010. The geography of climate change: implications for conservation biogeography. Diversity and Distributions 16, 476-487.</p><p>Ackerly, D.D., Kling, M.M., Clark, M.L., Papper, P., Oldfather, M.F., Flint, A.L., Flint, L.E., 2020. Topoclimates, refugia, and biotic responses to climate change. Frontiers in Ecology and the Environment 18, 288-297.</p><p>Araujo, M.B., Luoto, M., 2007. The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography 16.</p><p>Batllori, E., Parisien, M.-A., Parks, S.A., Moritz, M.A., Miller, C., 2017. Potential relocation of climatic environments suggests high rates of climate displacement within the North American protection network. Global Change Biology 23, 3219-3230.</p><p>Brito-Morales, I., García Molinos, J., Schoeman, D.S., Burrows, M.T., Poloczanska, E.S., Brown, C.J., Ferrier, S., Harwood, T.D., Klein, C.J., McDonald-Madden, E., Moore, P.J., Pandolfi, J.M., Watson, J.E.M., Wenger, A.S., Richardson, A.J., 2018. Climate Velocity Can Inform Conservation in a Warming World. Trends in Ecology & Evolution 33, 441-457.</p><p>Carter, T.R., Porter, J.H., Parry, M.L., 1991. Climatic warming and crop potential in Europe: Prospects and uncertainties. Global Environmental Change 1, 291-312.</p><p>Dobrowski, S.Z., Abatzoglou, J., Swanson, A.K., Greenberg, J.A., Mynsberge, A.R., Holden, Z.A., Schwartz, M.K., 2013. The climate velocity of the contiguous United States during the 20th century. Global Change Biology 19, 241-251.</p><p>Franklin, J., Davis, F.W., Ikegami, M., Syphard, A.D., Flint, L.E., Flint, A.L., Hannah, L., 2013. Modeling plant species distributions under future climates: how fine scale do climate projections need to be? Global Change Biology 19, 473-483.</p><p>Hamann, A., Roberts, D.R., Barber, Q.E., Carroll, C., Nielsen, S.E., 2015. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology 21, 997-1004. </p><p>Heikkinen, R.K., Leikola, N., Aalto, J., Aapala, K., Kuusela, S., Luoto, M., Virkkala, R., 2020. Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678.</p><p>Huntley, B., Green, R.E., Collingham, Y.C., Willis, S.G., 2007. A climatic atlas of European breeding birds. Durham University, The RSPB and Lynx Edicions, Barcelona.</p><p>Huntley, B., Collingham, Y.C., Willis, S.G., Green, R.E., 2008. Potential Impacts of Climatic Change on European Breeding Birds. Plos One 3.</p><p>Klok, E.J., Klein Tank, A.M.G., 2009. Updated and extended European dataset of daily climate observations. International Journal of Climatology 29, 1182-1191.</p><p>Lenoir, J., Hattab, T., Pierre, G., 2017. Climatic microrefugia under anthropogenic climate change: implications for species redistribution. Ecography 40, 253-266.</p><p>Luoto, M., Heikkinen, R.K., Pöyry, J., Saarinen, K., 2006. Determinants of biogeographical distribution of butterflies in boreal regions. Journal of Biogeography 33, 1764-1778.</p><p>Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., Wilbanks, T.J., 2010. The next generation of scenarios for climate change research and assessment. Nature 463, 747-756.</p><p>R Development Core Team, 2011. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing).</p><p>Skov, F., Svenning, J.-C., 2004. Potential impact of climatic change on the distribution of forest herbs in Europe. Ecography 27, 366-380.</p><p>Sykes, M.T., Prentice, I.C., Cramer, W., 1996. A bioclimatic model for the potential distributions of north European tree species under present and future climates. Journal of Biogeography 23, 203-233.</p><p>Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2012. An Overview of CMIP5 and the Experiment Design. Bulletin of the American meteorological Society 93, 485-498.</p><p>Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society Series B 73, 3-36.</p><p> </p>
Ultrasonic velocity measurements of lunar regolith simulant at low confining pressures with variable ice content
<p>This dataset was created by Christopher Chance Amos during completion of a PhD degree in Space Resources</p><p>at Colorado School of Mines. This data was collected during Spring 2023.</p><p> </p><p>This dataset includes compressional and shear raw collected waveforms as well as interpreted velocities</p><p>from first-break picking. See the README files in subdirectories for explanations of individual files.</p><p> </p><p>The purpose of this dataset is to serve as a foundation and calibration for seismic modeling of the lunar</p><p>near-surface. These models will be used to determine if seismic methods are feasible for characterizing</p><p>the quantity and form of lunar subsurface ice deposits.</p>
Subglacial Drainage Evolution Modulates Seasonal Ice Flow Variability of Three Tidewater Glaciers in Southwest Greenland - velocity and plume data
<p>This file contains four datasets:</p> <p>- ice velocity estimates of the area surrounding and including Kangiata Nunata Sermia, southwest Greenland, derived from Landsat imagery and from Sentinel 1 imagery</p> <p>- plume observations in this area</p> <p>- subglacial discharge at the terminus</p> <p>- glacier terminus positions</p> <p>A more complete description of these datasets will be provided in the publication with the same name (currently in review). The methods used to measure glacier terminus positions are described in https://doi.org/10.5194/esurf-6-551-2018 </p>
Data for Individual and combined impacts of carbon dioxide enrichment, heatwaves, flow velocity variability and fine sediment deposition on stream invertebrate communities
Open the record for dataset details and reuse information.
Reliability of Respiratory Variability of Maximal Aortic Blood Flow Velocity Obtained by Ultrasonography-doppler in Patients Followed Up with a Diagnosis of Shock in Intensive Care Unit for Evaluating
ClinicalTrials.gov study NCT06845280. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Dataset - Solving Seismic Wave Equation on Variable Velocity Models with Fourier Neural Operator
<p>The directory contains velocity models from OpenFWI dataset collection. These velocity models are the inputs of the FNO-based sovlers in the paper <strong><em>Solving Seismic Wave Equation on Variable Velocity Models with Fourier Neural Operator</em></strong>.</p>
Respiratory Variability in Aortic Blood Velocity Measured by Suprasternal View as an Indicator of Fluid Responsiveness
ClinicalTrials.gov study NCT02791984. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Customization of Cardiac Readaptation Based on Force-velocity Profile and Heart Rate Variability
ClinicalTrials.gov study NCT06107452. IPD Sharing: NO. Countries: 1. Publications: 0.
The Role of Left Ventricular Outflow Tract (LVOT) Velocity-time Integral (VTI) and Respiratory Variability at Peak Rate (Vpeak) in Predicting Hypotension After General Anesthesia Induction
ClinicalTrials.gov study NCT07121699. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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