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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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Supplementary data: Hazard from Himalyan glacier lake outburst floods

Authors: Veh, Georg; Korup, Oliver; Walz, Ariane;

Supplementary data: Hazard from Himalyan glacier lake outburst floods

Abstract

We provide supplementary data to the manuscript by Veh, G., Korup, O., and Walz A.: "Hazard from Himalayan glacier lake outburst floods". These datasets (file ending .rds) are ready to use within the R software for statistical programming. Please copy all files into one single directory and follow the instructions given in the scripts. You can find the following scripts on the associated Github page: https://github.com/geveh/GLOFhazard bayes_lm_piecewise_regression_stan.R lm_piecewise_const.stan R_Script_lake_area_vs_max_depth.R R_Script_Hazard_from_GLOFs_PNAS_supp.R HDIofMCMC.R Outputs within the scripts are written to disk, again as R-objects, if not suppressed by the user. Make sure you have at least 100 GB of free disk space.

{"references": ["S. B. Maharjan, et al., The status of glacial lakes in the Hindu Kush Himalaya-ICIMOD Research Report 2018/1 (2018). ICIMOD Res. Rep. (2018).", "J. E. O'Connor, R. A. Beebee, \"Floods from natural rock-material dams\" in Megaflooding on Earth and Mars, D. M. Burr, P. A. Carling, V. R. Baker, Eds. (Cambridge University Press, 2009), pp. 128\u2013171.", "J. S. Walder, J. E. O'Connor, Methods for predicting peak discharge of floods caused by failure of natural and constructed earthen dams. Water Resour. Res. 33, 2337\u20132348 (1997).", "J. K. Kruschke, Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. 2nd Edition. Academic Press / Elsevier (2014)."]}

Related Organizations
Keywords

Bayesian robust regression, piecewise regression, Dam-break floods, GLOF, R, Hazard, STAN

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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