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7 results for “Drought 2018”

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zenodo36/100

Data and code for the publication "Dieback of Calluna vulgaris in a coastal heathland after the 2018 extreme drought"

<p>Original data and code for the publication "Dieback of Calluna vulgaris in a coastal heathland after the 2018 extreme drought"</p>

opencc-by-4.0Nov 2024View details →
dryad36/100

Data from: The impacts of climate and the extreme drought in 2018 on population growth in Swedish moth species

Open the record for dataset details and reuse information.

publicFeb 2025View details →
dryad32/100

Early-wilted forest following the Central European 2018 extreme drought

<p><span>During the summer of 2018, Central Europe experienced the most extreme drought and heat wave on record, leading to widespread early leaf-shedding and die-offs in forest trees. We quantified such early-wilting responses by associating Sentinel-2 time-series statistics of the Normalized Difference Vegetation Index with visually classified orthophotos, using a random forest classifier. The predictions of our classifier achieved a high accuracy of 0.90 </span><span><span>±0.014 and estimated</span></span><span> the area of affected forest at </span><span><span>21'500 ±2800 km<sup>2</sup></span></span><span>. Early wilting was especially prevalent in eastern and central Germany and in the Czech Republic and it was related to high temperatures and low precipitation at large-scales, and small to medium-sized trees, steep slopes, and shallow soils at fine-scales. The present dataset includes spatial predictons of 2018 early-wilting presence/absence for entire Central Europe (</span><em><span>c</span></em><span>. 800'000 km</span><sup><span>2</span></sup><span>) at 10×10 m resolution. It may be used for high-resolution studies of early-wilting patterns, to study how factors like physiology or species identity relate to early-wilting patterns, and/or as testbed for alternative approaches quantifying water stress in forests. </span></p>

opencc-zeroOct 2020View details →
zenodo32/100

Model calibration and streamflow simulations for the extreme drought event of 2018 on the Rhine River Basin using WRF-Hydro 5.2.0

<p>This repository contains the data and software used for the study "Model calibration and streamflow simulations for the extreme drought event of 2018 on the Rhine River Basin using WRF-Hydro 5.2.0." The data and model were used to simulate an extreme low-water event in the River Rhine Basin.</p> <p>This archive contains the following:</p> <p><strong>wrf_hydro_nwm_public-5.2.0.tar.gz</strong>: Model code of the hydrological model WRF-Hydro. (Source: https://ral.ucar.edu/projects/wrf_hydro/model-code)</p> <p><strong>ERA5_Dataset_2016_2018.zip</strong>: ERA5 Reanalysis data&nbsp; and modified for the domain of the project, it includes all the mandatory variables necessary to run the model&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; (Source:https://doi.org/10.24381/cds.bd0915c6)</p> <p><strong>GRDC_HydrologicalData_Rhine_Basin.zip</strong>: Daily stremaflow from gauges in the Rhine River from the Global Runoff Dataset Center (GRDC) (Source: https://portal.grdc.bafg.de/applications/public.html?publicuser=PublicUser#dataDownload/Stations)</p> <p><strong>RegriddedFormatData.sh: </strong>Bash file to create the the input data for the regridding scripts using the ERA5 data. (Source: Campoverde, A.)</p> <p><strong>ESMFregrid_NLDAS.tar.gz</strong>: Earth System Modeling Framework (ESMF) Regridding Scripts (Source: https://ral.ucar.edu/projects/wrf_hydro/pre-processing-tools#regridding2)</p> <p><strong>wrf_hydro_arcgis_preprocessor-5.2.0.tar.gz</strong>: ArcGIS tools for preparing WRF-Hydro Routing Grids. (Source: https://ral.ucar.edu/projects/wrf_hydro/pre-processing-tools#preprocessing1)</p> <p><strong>eu_dem_3s.zip</strong>: Digital Elevation Model for Europe from HydroSHEDS - 3" (~90m) (Source: https://www.hydrosheds.org/hydrosheds-core-downloads)</p> <p><strong>S2GLC_Europe_2017_v1.2_grey.zip</strong>: Land Cover data used to create spatially distributed hydrological parameters from WRF-Hydro (RETDEPRTFAC, REFKDT, SLOPE) (Source: https://s2glc.cbk.waw.pl/extension)</p> <p><strong>WRF_Hydro_Setup_withLakeScheme.zip</strong>: Necesary files for WRF-Hydro model when considering the Lake Scheme. (Source: Campoverde, A.)</p> <p><strong>WRF_Hydro_Setup_withoutLakeScheme.zip</strong>: Necessary files for the WRF-Hydro model when <strong>not</strong> considering the Lake Scheme. (Source: Campoverde, A.)</p> <p><strong>ERA5_Land_Soil_Moisture_Dataset_2016_2018.zip</strong>: ERA5 Land data for the domain of the project includes the values of soil moisture for the period 2016-2018. (Source:https://doi.org/10.24381/cds.e2161bac)</p> <p>With this repository, we aim to provide to the comminity the necessary tools to replicate the experiments that led to the calibration of WRF-Hydro and the results of our study.&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Aug 2024View details →
dryad32/100

Early-wilted forest following the Central European 2018 extreme drought

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publicOct 2020View details →
dryad28/100

Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought

<p>The summer drought of 2018 caused major damages to forest ecosystems. In this dataset, various environmental variables (precipitation anomaly, temperature anomaly, climatic water balance anomaly, elevation, slope, aspect, exposition, potential direct incedent radiation, distance to the forest edge, tree type, and species heterogeneity) have been stratified into ten sections and the proportion of forest pixels with severe changes (equal or more than 10% either positive or negative) in the normalized difference water index (NDWI) of the forest canopy from 2017-2018, 2018-2019, and 2017-2019 in Switzerland declared.</p>

opencc-zeroMay 2022View details →
dryad28/100

Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought

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publicMay 2022View details →

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