Space-time inconsistencies in the dynamics of water coverage: tracking walking floods
- 1. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA), Facultad de Agronomía, Universidad de Buenos Aires, CONICET, Buenos Aires, Argentina
- 2. Grupo de Estudios Ambientales – IMASL, Universidad Nacional de San Luis & CONICET, San Luis, Argentina
Description
This folder contains the R scripts and associated datasets to replicate the work developed in the article "Space-time inconsistencies in the dynamics of water coverage: tracking walking floods" under review at Geophysical Research Letters.
Global flooded extent was derived from JRC's Global Surface Water dataset v1.4 (Pekel et al., 2016) available in the Google Earth Engine Data Catalog (https://developers.google.com/earth-engine/datasets). A version of the Google Earth Engine code for quantifying flooding displacement in one landscape (i.e., cell) is found at: https://code.earthengine.google.com/67137f175ff3ef0c73ccd6d07338eaac?noload=true
Anthromes were downloaded from https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/G0QDNQ (Ellis et al., 2019). River segment characterization was extracted from https://zenodo.org/record/2582500 based on Global River Width from Landsat (Frasson et al., 2019; Allen & Pavelsky, 2018). Global Lakes and Wetlands Database Level 3 (GLWD-3; Lehner & Döll, 2004) was downloaded from https://www.worldwildlife.org/publications/global-lakes-and-wetlands-database-lakes-and-wetlands-grid-level-3. The aridity index was calculated based on TerraClimate long-term averages of annual precipitation-to-potential evapotranspiration ratios (Abatzoglou et al., 2018), while terrain attributes were calculated based on Global Multi-resolution Terrain Dataset (USGS), both available in the Google Earth Engine Data Catalog.
Files
anthromes_aggregated.csv
Files
(5.7 MB)
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Additional details
References
- Pekel, J.-F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418–422. https://doi.org/10.1038/nature20584
- Ellis, Erle; Klein Goldewijk, Kees, 2019, "Anthromes 12K Full Dataset", https://doi.org/10.7910/DVN/G0QDNQ, Harvard Dataverse, V3 [Dataset].
- Frasson, Renato Prata de Moraes, Pavelsky, Tamlin M., Fonstad, Mark A., Durand, Michael T., Allen, George H., Schumann, Guy, Lion, Christine, Beighley, R. Edward, & Yang, Xiao. (2019). Global database of river width, slope, catchment area, meander wavelength, sinuosity, and discharge [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.2582500
- Lehner, B., & Döll, P. (2004). Development and validation of a global database of lakes, reservoirs and wetlands. Journal of hydrology, 296(1-4), 1-22.
- Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 5, 1–12. https://doi.org/10.1038/sdata.2017.191