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172 results for “global scale”
Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
<p><strong>Overview:</strong> The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MA<sub>Min</sub>; total area, 6.5 × 106 km<sup>2</sup>), mean annual maximum (MA<sub>Max</sub>; 12.1 × 106 km<sup>2</sup>), and long-term maximum (LT<sub>Max</sub>; 17.3 × 106 km<sup>2</sup>). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.</p> <p><strong>Projection:</strong> WGS84 (EPSG:4326)</p> <p><strong>Geographic extent:</strong></p> <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> <p><strong>Spatial resolution: </strong>15 arc-second (500m at equator)</p> <p><strong>Legend</strong> (for discrete pixel values):</p> <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MA<sub>Min</sub>)</li> <li>2 = Mean Annual Maximum (MA<sub>Max</sub>)</li> <li>3 = Long Term Maximum (LT<sub>Max</sub>)</li> </ul>
GPP: Site-scale and global model outputs from P-model used for Stocker et al. (2019) Nature Geosci.
<p><strong>Data from article Stocker et al. (in review) *Nature Geosci.*</strong></p> <p>The datasets provided here include:</p> <ul> <li>Site-level GPP model results from the P-model (Wang et al., 2017)</li> <li>Model outputs from global simulations with the P-model (Wang et al., 2017) as implemented for the study by Stocker et al. (2019)</li> </ul> <p>This data may be used to partly reproduce results presented in Stocker et al. (2019) <em>Nature Geosci</em>. "Partly" because we used data for our analysis that was not open access but was confidentially shared with us. This includes remote sensing-based GPP estimates from the BESS and VPM models. Other open access data that was used for the analysis may not be distributed under this DOI. This includes FLUXNET 2015 data and MODIS data.</p> <p>For reproducing results of Stocker et al. (2019) regarding site-scale evaluations, run for example the scripts `plot_bias_all.R` and `plot_bias_problem.R`, available from <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="http://doi.org/10.5281/zenodo.1423328">Zenodo</a>, using CSV files provided here (see comments in scripts). For more insight, including analysis of global simulation outputs, see RMarkdown file `si_soilm_global.Rmd`. This renders the supplementary information PDF document provided along with Stocker et al. (2019), which is available also on <a href="http://rpubs.com/stineb/si_soilm_global2">RPubs</a>.</p> <p>The present datasets are prepared by script `prepare_data_openaccess.R ` on <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="https://zenodo.org/record/1286966#.W6TFipMzbUI">Zenodo</a>.</p> <p><strong>Data description</strong></p> <p><em>Site-level data</em></p> <p>Data is provided as CSV files:</p> <ul> <li>`gpp_daily_fluxnet_stocker18natgeo.csv`: Daily data for full time series (not including MODIS GPP)</li> <li>`gpp_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods corresponding to MODIS dates (including MODIS GPP)</li> <li>`gpp_alg_daily_fluxnet_stocker18natgeo.csv`: Data filtered to periods with substantial soil moisture effects ("fLUE droughts" following Stocker et al. (2018a))</li> <li>`gpp_alg_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods and filtered to periods with substantial soil moisture effects.</li> </ul> <p>Each column is a variable with the following name and units (not all variables are available in all files):</p> <ul> <li>`site_id`: FLUXNET site ID </li> <li>`date`: Date of measurement, units: YYYY-MM-DD</li> <li>`gpp_pmodel` and `gpp_modis`: Simulated GPP from the P-model and MODIS (see Stocker et al. (2018b), Methods, RS models), units: g C m-2 d-1 (mean across 8 day periods in respective files)</li> <li>`aet_splash`: Simulated actual evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`pet_splash`: Simulated potential evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`soilm_splash`: Soil moisture simulated by the SPLASH model (Davis et al., 2017), normalised to vary between zero and one at the maximum water holding capacity, unitless.</li> <li>`flue`: fLUE estimate from Stocker et al. (2018). Estimates soil moisture stress on light use efficiency from flux data, unitless.</li> <li>`beta_a`, `beta_b`, and `beta_c`: Empirical soil moisture stress, used as multiplier to simulated GPP as described in Stocker et al. (2018b), unitless.</li> </ul> <p><em>Global P-model simulation outputs</em></p> <p>GPP and soil moisture output is provided as NetCDF files for simulations s0, and s1b (see Stocker et al. (2018b)). All meta information is provided therein. Files for simulation s1b are names as follows (for outputs from other simulations replace s1b with other simulation name). The fraction of each gridcell covered by land (not open water or ice) is given by separate file `s1b_fapar3g_v2_global.fland.nc`.</p> <ul> <li>`s1b_fapar3g_v2_global.d.gpp.nc`: Daily GPP from simulation s1b.</li> <li>`s1b_fapar3g_v2_global.d.wcont.nc`: Daily soil moisture from simulation s1b (is identical in other simulations, therefore not provided.)</li> </ul> <p>Due to limited total file size allowed for uploads to Zenodo, only outputs from s1b are provided here. Other outputs may be obtained upon request addressed to benjamin.stocker@gmail.com. </p> <p><strong>References</strong></p> <p>Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geoscientific Model Development 10, 689–708 (2017).<br> Hufkens, K. khufkens/gee_subset: Google Earth Engine subset script & library. (2017). doi:10.5281/zenodo.833789Running, S. W. et al. A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. Bioscience 54, 547–560 (2004).<br> Stocker, B. et al., Quantifying soil moisture impacts on light use efficiency across biomes, New Phytologist, doi: 10.1111/nph.15123 (2018a).<br> Stocker, B. et al., Satellite monitoring underestimates the impact of drought on terrestrial primary productivity, Nature Geoscience (2019).<br> Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat Plants 3, 734–741 (2017).<br> </p>
Dynamically coupled kinetic chemistry in brown dwarf atmospheres I. Performing global scale kinetic modelling
<p>Gifs and Exo-FMS GCM output from the 3D brown dwarf atmospheric simulations in Lee, Tan and Tsai (2023). </p> <p>Animated gifs for each effective temperature (Teff - first number in filename) of the brown dwarf (OLR and CH4 VMR). The gifs frames are every hour of simulation for 4 simulated days.</p> <p>Exo-FMS GCM output in netCDF format containing the 3D T-p structure and chemical results from the coupled mini-chem and GCM model for each Teff simulation (number in filename).</p> <p>`average' is the averaged output of the last 100 days.</p> <p>`daily' is the snapshot at the end of the simulation.</p>
Data supporting manuscript "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble"
<p>Data supporting the results presented in the article Milovac et al: "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble".</p> <p>1. data_raw.tar contains annual and seasonal, global and regional (i.e. over ocean IPCC regions and ocean biomes), mean sea surface and near surface temperatures, calculated for the selected 26 CMIP6 global climate models (GCMs) at low resolution (listed in the file models_low_res.txt) and 1 GCM at high resolution (listed in the file models_high_res.txt). The original files, downloaded from one of the ESGF data centers, were all interpolated onto a common grid with the 1-degree resolution for low-resolution output and the 0.25-degree resolution for high-resolution output. The output was generated using the cdo tool (<a href="https://zenodo.org/record/7112925">https://zenodo.org/record/7112925</a>).</p> <p>2. data_txt.tar contains the results used to obtain all the figures given in the article.</p>
High-frequency light attenuation measurements in 35 lakes: companion data from "Coefficients in Taylor’s Law increase with the time scale of water clarity measurements in a global suite of lakes"
Identifying the scaling rules describing ecological patterns across time and space is a central challenge in ecology. Taylor’s Law of fluctuation scaling, which states that the variance of a population is proportional to a positive power of the mean, has been widely observed in population dynamics and characterizes variability in multiple scientific domains. However, it is unclear if this phenomenon accurately describes ecological patterns across many orders of magnitude in time, and therefore links otherwise disparate observations. This dataset uses light attenuation observations from 10,531 days of high-frequency measurements in 35 globally distributed lakes to test this unknown. We focus on water clarity as an integrative ecological characteristic that responds to both biotic and abiotic drivers. We provide documentation that variations in ecological measurements across diverse sites and temporal scales exhibit variance patterns consistent with Taylor’s Law, and that model coefficients increase in a predictable yet non-linear manner with decreasing observation frequency.
Compound flood potential from river discharge and storm surge extremes at the global scale
<p>This dataset presents the results presented in <a href="https://doi.org/10.5194/nhess-20-489-2020">Couasnon et al. (2019) - Measuring compound flood potential from river discharge and storm surge extremes at the global scale</a>. For more information about the methods, please refer to the paper. This dataset was created using as input <a href="https://zenodo.org/record/3552820#.XmIdoVxKhaQ">time series of discharge and maximum storm surge at river mouths globally from 1980 - 2014</a>.</p> <p>If using this data, please cite: </p> <p>Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I. E., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489–504, https://doi.org/10.5194/nhess-20-489-2020, 2020.</p>
Global scale leaf broadband optical properties derived from CliMA Land and associated CESM simulations
<p>Leaf level broadband reflectance and transmittance computed from leaf traits.</p> <ul> <li>clm_refl_tran_1m_weighted.nc: monthly data (144*96 pixels)</li> <li>surfdata_CMIP6_fluspect_v3.nc: surface data to run CESM (144*96 pixels)</li> </ul> <p>Global scale simulation results</p> <ul> <li>research_data_coupled_future_v2.nc: CESM coupled future simulations</li> <li>research_data_coupled_history_v2.nc: CESM coupled historical simulations</li> <li>research_data_uncoupled_history_v2.nc: CESM uncoupled future simulations</li> <li>research_data_uncoupled_ssp_v2.nc: CESM uncoupled SSP245 and SSP585 simulations</li> </ul> <p>Code changes</p> <ul> <li>SurfaceAlbedoMod.F90: modified CLM module</li> <li>Julia-and-Python-Code.tar.gz: code used for processing the data and plot the figures</li> </ul>
An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')
<p>These data were produced as part of the study "An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')" (in press in Earth's Future, https://doi.org/10.1029/2023EF003895). We provide raster data at 30 arc seconds spatial resolution (folder 'raster') and vector and table data per administrative unit (folder 'admin') of five social vulnerability variables and the final Global Empirical Social Vulnerability Index (GlobE-SoVI) calculated from the five variables. Please see 'overview_table.pdf' for names and units.</p> <p>The code for data processing and analysis is available at https://github.com/lena-reimann/GlobE-SoVI (https://doi.org/10.5281/zenodo.10671539).</p>
Rapid in situ diversification rates in Rhamnaceae explain the parallel evolution of high diversity in temperate biomes from global to local scales
<p>The macroevolutionary processes that have shaped biodiversity across the temperate realm remain poorly understood and may have resulted from evolutionary dynamics related to diversification rates, dispersal rates, and colonization times, closely coupled with Cenozoic climate change.</p> <p>We integrated phylogenomic, environmental ordination, and macroevolutionary analyses for the cosmopolitan angiosperm family Rhamnaceae to disentangle the evolutionary processes that have contributed to high species diversity within and across temperate biomes.</p> <p>Our results show independent colonization of environmentally similar but geographically separated temperate regions mainly during the Oligocene, consistent with the global expansion of temperate biomes. High global, regional, and local temperate diversity was the result of high <em>in</em> <em>situ</em> diversification rates, rather than high immigration rates or accumulation time, except for Southern China, which was colonized much earlier than other regions. The relatively common lineage dispersals out of temperate hotspots highlights strong source-sink dynamics across the cosmopolitan distribution of Rhamnaceae.</p> <p>The proliferation of temperate environments since the Oligocene may have provided the ecological opportunity for rapid <em>in</em> <em>situ</em> diversification of Rhamnaceae across the temperate realm. Our study illustrates the importance of high <em>in</em> <em>situ</em><strong> </strong>diversification rates for the establishment of modern temperate biomes and biodiversity hotspots across spatial scales.</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2011_2015)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2001_2005)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Text-fig. 1. a: Po Plain and foothills of the Northern Apennine in Northern Italy (inset) with the location of Oriolo (black star) and other Early and Middle Pleistocene plant localities, Enza and Stirone. Red lines indicate the frontal thrust arcs (modified from Martinetto et al. 2015). b: The "La Salita" section, Oriolo and chronology of the two "Sabbie gialle" cycles based on large mammals and palaeomagnetic correlation (modified from Toniato et al. 2017; IMMS 2020* [Italian Mediterranean Marine Stages] updated from Cohen and Gibbars 2020; GTS 2021* [Global Time Scale] updated from Head et al. 2021). c: Quarry "La Salita", Oriolo, in 1987. Main unconformities (U) separating the two "Sabbie gialle" cycles and terrestrial deposits on top are shown. Leaf symbols indicate the positions of some of the layers rich in fossil leaves (photo by G. B. Vai, modified). d: Surroundings of Faenza with the location of Oriolo and adjacent coeval sites yielding plant macrofossils. in The Late Early Pleistocene Flora Of Oriolo, Faenza (Italy): Assembly Of The Modern Forest Biome
Text-fig. 1. a: Po Plain and foothills of the Northern Apennine in Northern Italy (inset) with the location of Oriolo (black star) and other Early and Middle Pleistocene plant localities, Enza and Stirone. Red lines indicate the frontal thrust arcs (modified from Martinetto et al. 2015). b: The "La Salita" section, Oriolo and chronology of the two "Sabbie gialle" cycles based on large mammals and palaeomagnetic correlation (modified from Toniato et al. 2017; IMMS 2020* [Italian Mediterranean Marine Stages] updated from Cohen and Gibbars 2020; GTS 2021* [Global Time Scale] updated from Head et al. 2021). c: Quarry "La Salita", Oriolo, in 1987. Main unconformities (U) separating the two "Sabbie gialle" cycles and terrestrial deposits on top are shown. Leaf symbols indicate the positions of some of the layers rich in fossil leaves (photo by G. B. Vai, modified). d: Surroundings of Faenza with the location of Oriolo and adjacent coeval sites yielding plant macrofossils.
Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021.
Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies
<p><span>Here we generated the global sand dune pattern map at a resolution of 30 m, named GSDP30. The GSDP30 map encompasses 11 types of sand dune patterns (SDPs): simple crescentic dunes, compound-complex crescentic dunes, simple linear dunes, compound-complex linear dunes, dome dunes, star dunes, parabolic dunes, dendritic dunes, network dunes, sand sheets, and others. The map is divided into 331 Tiff tiles, each characterized by a size of 15,360 × 15,360 pixels and named according to the longitude and latitude coordinates of its upper-left corner.</span></p>
Forest resilience to global warming is strongly modulated by local-scale topographic, microclimatic and biotic conditions
<p>Resilience of endangered rear edge populations of cold-adapted forests in the Mediterranean basin is increasingly altered by extreme heatwave and drought pressures. It remains unknown, however, whether microclimatic variation in these isolated forests could ultimately result in large intra-population variability in the demographic responses, allowing the coexistence of contrasting declining and resilient trends across small topographic gradients. Multiple key drivers promoting spatial variability in the resilience of rear edge forests remain largely unassessed, including amplified and buffered thermal exposure induced by heat waves along topographic gradients, and increased herbivory pressure on tree saplings in defaunated areas lacking efficient apex predators. Here we analysed whether indicators of forest resilience to global warming are strongly modulated by local-scale topographic, microclimatic and biotic conditions.</p> <p>We studied a protected rear edge forest of sessile oak (<em>Q. petraea</em>), applying a suite of 20 indicators of resilience of tree secondary growth, including multidecadal and short-term indices. We also analysed sapling recruitment success, recruit/adult ratios and sapling thermal exposure across topographic gradients. We found large within population variation in secondary growth resilience, in recruitment success and in thermal exposure of tree saplings to heatwaves, and this variability was spatially structured along small-scale topographical gradients. Multidecadal resilience indices and curves provide useful descriptors of forest vulnerability to climate warming, complementing assessments based in the analysis of short-term resilience indicators. Species-specific associations of trees with microclimatic variability are reported.</p> <p>Biotic factors are key in determining long-term resilience in climatically-stressed rear edge forests, with strong limitation of sapling recruitment by increased roe deer and wild boar herbivory. Our results also support non-stationary effects of climate determining forest growth responses and resilience, showing increased negative effects of warming and drought over the last decades in declining stands.</p> <p>Our findings do not support scenarios predicting spatially homogeneous distributional shifts and limited resilience in rear-edge populations, and are more supportive of scenarios including spatially heterogeneous responses, characterised with contrasting intra-population trends of forest resilience. We conclude that forest resilience responses to climate warming are strongly modulated by local-scale microclimatic, topographic and biotic factors. Accurate predictions of forest responses to changes in climate would therefore largely benefit from the integration of local-scale abiotic and biotic factors.</p>
DATASET: Digital humanities at global scale
<p>Dataset to analyse the scientific production of the Digital humanities at global scale. Databases consulted: App Dimensions, Web of Science (WoS), Scopus.</p> <p>Dataset to analyze the scientific production with reference: </p> <p>Bocanegra Barbecho, L., Ros Muñoz, S., González-Blanco García, E., & Toscano, M. (2023). Digital humanities at global scale. Interdisciplinary Science Reviews, 48(3), 446–459. https://doi.org/10.1080/03080188.2023.2193799</p>
Data for: Historical and contemporary processes drive global phylogenetic structure across geographical scales: Insights from bat communities
<p><strong>Aim</strong>: Patterns of evolutionary relatedness among co-occurring species are driven by scale-dependent contemporary and historical processes. Yet, we still lack a detailed understanding of how these drivers impact the phylogenetic structure of biological communities. Here, we focused on bats – one of the most speciose and vagile groups of mammals – and test the predictions of three general biogeographical hypotheses that are particularly relevant to understanding how paleoclimatic stability, local diversification rates, and geographical scales shaped their present-day phylogenetic community structure.</p> <p><strong>Location</strong>: Worldwide, across restrictive geographical extents: global, east-west hemispheres, biogeographical realms, tectonic plates, biomes, and ecoregions.</p> <p><strong>Time period</strong>: Last Glacial Maximum (~22,000 years ago) to the present.</p> <p><strong>Major taxa studied</strong>: Bats (Chiroptera)</p> <p><strong>Methods</strong>: We estimated bat phylogenetic community structure across restrictive geographical extents and modelled it as a function of paleoclimatic stability, and in situ net diversification rates.</p> <p><strong>Results</strong>: Limiting geographical extents from larger to smaller scales strongly changed the phylogenetic structure of bat communities. The magnitude of these effects is less noticeable in the western hemisphere, where frequent among-realm biota interchange could have been maintained through bats' adaptive traits. Highly phylogenetically related bat communities are generally more common in regions that changed less in climate since the last glacial maximum, supporting the expectation that stable climates allow for increased phylogenetic clustering. Finally, increased in situ net diversification rates are associated with greater phylogenetic clustering in bat communities.</p> <p><strong>Main conclusions</strong>: We show that the worldwide phylogenetic structure of bat assemblages varies as a function of geographical extents, dispersal barriers, paleoclimatic stability and in situ diversification. The integrative framework used in our study, which can be applied to other taxonomic groups, has proven useful to not only explain the evolutionary dynamics of community assembly but could also help tackle questions related to scale dependence in community ecology and biogeography.</p>
Codes and data related to the article: Renard et al. Floods and Heavy Precipitation at the Global Scale: 100-year Analysis and 180-year Reconstruction. Journal of Geophysical Research - Atmospheres.
<p>This package contains R codes and data related to the article:</p> <p>B. Renard, D. McInerney, S. Westra, M. Leonard, D. Kavetski, M. Thyer and J.-P. Vidal. Floods and Heavy Precipitation at the Global Scale: 100-year Analysis and 180-year Reconstruction. <em>Journal of Geophysical Research - Atmospheres</em>. DOI: <a href="https://doi.org/10.1029/2022JD037908">10.1029/2022JD037908</a></p> <p><strong>Analyses</strong></p> <p>This folder contains the R scripts used to set up models, analyse results and prepare figures. See README file for details.</p> <p><strong>ShinyApp</strong></p> <p>This folder contains an interactive Shiny App to explore the data and the results from the article.</p> <p>An online version can be found at <a href="https://hydroapps.recover.inrae.fr/HEGS-paper">https://hydroapps.recover.inrae.fr/HEGS-paper</a></p> <p> </p>
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