2018
DOI: 10.1038/sdata.2018.40
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A suite of global, cross-scale topographic variables for environmental and biodiversity modeling

Abstract: Topographic variation underpins a myriad of patterns and processes in hydrology, climatology, geography and ecology and is key to understanding the variation of life on the planet. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale research applications, however to date, such datasets are unavailable. Here we used the digital elevation model products of global 250 m GMTED2010 and near-global 90 m SRTM4.1dev to derive a suite of topog… Show more

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Cited by 522 publications
(434 citation statements)
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“…In places where most flooding is caused by precipitation, the spatial extent of synchronous precipitation provides an approximate upper bound on the spatial extent of synchronous flooding. Mean flood synchrony scales are inversely correlated (p < 0.001) with station elevation and terrain ruggedness (Amatulli et al, 2018), indicating that flooding tends to be more spatially coherent at lower altitudes and in flatter landscapes ( Figure S7). Topography, on the other hand, appears to substantially influence the flood synchrony scale.…”
Section: Resultsmentioning
confidence: 99%
“…In places where most flooding is caused by precipitation, the spatial extent of synchronous precipitation provides an approximate upper bound on the spatial extent of synchronous flooding. Mean flood synchrony scales are inversely correlated (p < 0.001) with station elevation and terrain ruggedness (Amatulli et al, 2018), indicating that flooding tends to be more spatially coherent at lower altitudes and in flatter landscapes ( Figure S7). Topography, on the other hand, appears to substantially influence the flood synchrony scale.…”
Section: Resultsmentioning
confidence: 99%
“…To account for the effects of elevation and topography, each checklist location was associated with elevation, eastness, and northness. These latter two topographic variables combine slope and aspect to provide a continuous measure describing geographic orientation in combination with slope at 1-km 2 resolution (Amatulli et al 2018). Each checklist was also linked to a series of covariates derived from the NASA MODIS land cover data (Friedl et al 2010).…”
Section: Predictor Datamentioning
confidence: 99%
“…We downloaded the current land surface climatic data with a resolution of 30 arc‐second from CHELSA (available at http://chelsa-climate.org), which contains average values of 19 predictor variables for the time period 1979–2013 (Karger et al, ; Table S2). In addition, we obtained elevation and slope at 30 arc‐second resolution from EarthEnv (available at http://www.earthenv.org/topography; Amatulli et al, ). We resampled all variables to 5 arc‐minutes.…”
Section: Methodsmentioning
confidence: 99%