Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2817-2020
https://doi.org/10.5194/hess-24-2817-2020
Research article
 | 
29 May 2020
Research article |  | 29 May 2020

Emerging climate signals in the Lena River catchment: a non-parametric statistical approach

Eric Pohl, Christophe Grenier, Mathieu Vrac, and Masa Kageyama

Viewed

Total article views: 2,615 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,853 716 46 2,615 162 35 41
  • HTML: 1,853
  • PDF: 716
  • XML: 46
  • Total: 2,615
  • Supplement: 162
  • BibTeX: 35
  • EndNote: 41
Views and downloads (calculated since 10 Sep 2019)
Cumulative views and downloads (calculated since 10 Sep 2019)

Viewed (geographical distribution)

Total article views: 2,615 (including HTML, PDF, and XML) Thereof 2,232 with geography defined and 383 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Apr 2024
Download
Short summary
Existing approaches to quantify the emergence of climate change require several user choices that make these approaches less objective. We present an approach that uses a minimum number of choices and showcase its application in the extremely sensitive, permafrost-dominated region of eastern Siberia. Designed as a Python toolbox, it allows for incorporating climate model, reanalysis, and in situ data to make use of numerous existing data sources and reduce uncertainties in obtained estimates.