605 research outputs found
soilm_global: Code for Stocker et al. (2019) Nature Geosci.
No full text<p>Code accompanying paper Stocker et al. (2019) <em>Nature Geosci</em>. For further information, see README and si_soilm_global.Rmd.</p>
GPP: Site-scale and global model outputs from P-model used for Stocker et al. (2019) Nature Geosci.
No full text<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 [email protected]. </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>
COVID-19 and the Internet: Lessons Learned
No full textThe COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit
sofun
No full textThis version of SOFUN was used for global simulations used for Stocker et al. (2019) GMD. It implements the following changes relative to the version used for Stocker et al. (2019) Nature Geosci.:
Included VPD calculation based on monthly Tmin and Tmax from CRU TS
Included pressure-dependency of gamma-star (CO2 compensation point) in pmodel(
GPP at FLUXNET Tier 1 sites from P-model
No full textGross primary production, simulated by the P-model for each FLUXNET 2015 Tier 1 site. The model was driven by site-specific meteorological forcing and MODIS FPAR, extracted for the pixel corresponding to the site location.
The CSV files contain simulated GPP values from different model setups conducted with the P-model and used for the publication Stocker et al. Geosci. Mod. Dev. (in review). One file is given for each temporal aggregation level (daily, 8-daily, annual, spatial [= mean annual value by site], and mean seasonal cycle [= mean per day-of-year]. Each file contains output from all model setups presented in Stocker et al. (2019), as given by column setup.
The data differs slightly for each file:
Daily gpp_pmodel_fluxnet2015_stocker19gmd_daily.csv:
sitename: A character specifying the site ID following the naming given by FLUXNET 2015.
date: YYYY-MM-DD), date_start (in _8daily, YYYY-MM-DD specifying the first day of the respective 8-day period), year (in _annual, YYYY), doy (in __meanseason, specifying the day-of-year),
gpp: Simulated gross primary production, in units of g C m-2 d-1
setup: A character specifying the model setup name used in Stocker et al. (2019). See also below.
8-daily gpp_pmodel_fluxnet2015_stocker19gmd_8daily.csv:
sitename: A character specifying the site ID following the naming given by FLUXNET 2015.
date_start : YYYY-MM-DD specifying the first day of the respective 8-day period
gpp: Simulated gross primary production, in units of g C m-2 d-1
setup: A character specifying the model setup name used in Stocker et al. (2019). See also below.
Annual gpp_pmodel_fluxnet2015_stocker19gmd_annual.csv:
sitename: A character specifying the site ID following the naming given by FLUXNET 2015.
year: YYYY
gpp: Simulated gross primary production, in units of g C m-2 yr-1
setup: A character specifying the model setup name used in Stocker et al. (2019). See also below.
Spatial gpp_pmodel_fluxnet2015_stocker19gmd_spatial.csv:
sitename: A character specifying the site ID following the naming given by FLUXNET 2015.
gpp: Simulated gross primary production, in units of g C m-2 yr-1
setup: A character specifying the model setup name used in Stocker et al. (2019). See also below.
Mean seasonal cycle gpp_pmodel_fluxnet2015_stocker19gmd_meanseason.csv:
sitename: A character specifying the site ID following the naming given by FLUXNET 2015.
doy: day-of-year
gpp: Simulated gross primary production, in units of g C m-2 d-1
setup: A character specifying the model setup name used in Stocker et al. (2019). See also below.
</p
eval_pmodel
No full textScripts for calibrating and evaluating the P-model as used for Stocker et al. (2020) Geoscientific Model Development. (P-model v1.0: An optimality-based light use efficiency model for simulating ecosystem gross primary production).
This contains a zipped folder, exported from GitHub repository eval_pmodel release at tag v2 (corresponding to commit b718cc2)(https://github.com/stineb/eval_pmodel/releases/tag/v2). </p
fLUE
No full text<p>This dataset contains fLUE data as described in Stocker et al., (2018) <em>New Phytologist</em>. fLUE is derived from the FLUXNET 2015 dataset, Tier 1, daily. Only sites are included where the method for quantifying fLUE satisfied performance criteria (see Stocker et al. 2018). </p>
<p> </p>
<ul>
<li><em>site</em>: Site name (ID) from the FLUXNET network</li>
<li><em>date</em>: DD/MM/YY</li>
<li>year: year</li>
<li><em>doy</em>: day of year</li>
<li><em>fLUE</em>: unitless, fraction of actual over potential light use efficiency, derived from artificial neural networks. This quantifies the fractional reduction in light use efficiency due to soil moisture (1 = no reduction).</li>
<li><em>is_flue_drought</em>: TRUE if the data is identified as a 'drought' based on deviation of fLUE from 1 (see Stocker et al., 2018)</li>
<li><em>cluster</em>: sites are assigned to clusters based on their typical parallel evolution of greenness and fLUE throughout drought events. cDD: 'drought deciduous', cGR: 'evergreen', cLS: 'low sensitivity', cNA: 'not affected'. </li>
</ul>
rsofun
No full textVersion of rsofun used for Stocker et al. (2020) GMD. Wraps the SOFUN model (separate repository!) though input/output writing via files. No direct wrapping of the FORTRAN code within the rsofun package is implemented at this release.
This contains a zipped folder, exported from GitHub repository rsofun release at tag v1.0.wrap_sofun (corresponding to commit defbf93) (https://github.com/stineb/rsofun/releases/tag/v1.0.wrap_sofun). </p
Integrated mid-infrared spectroscopic sensing technology
No full textAuthor Gerald Stocker, MScDissertation Johannes Kepler Universität Linz 2022Arbeit nach Ablauf der Sperre auf den öffentlichen PCs in den Bibliotheken der JKU+Medizin abrufba
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