Articles | Volume 26, issue 17
https://doi.org/10.5194/hess-26-4553-2022
https://doi.org/10.5194/hess-26-4553-2022
Research article
 | 
13 Sep 2022
Research article |  | 13 Sep 2022

Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria

Johannes Laimighofer, Michael Melcher, and Gregor Laaha

Viewed

Total article views: 2,047 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,586 412 49 2,047 31 28
  • HTML: 1,586
  • PDF: 412
  • XML: 49
  • Total: 2,047
  • BibTeX: 31
  • EndNote: 28
Views and downloads (calculated since 10 May 2022)
Cumulative views and downloads (calculated since 10 May 2022)

Viewed (geographical distribution)

Total article views: 2,047 (including HTML, PDF, and XML) Thereof 1,913 with geography defined and 134 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 16 Jun 2024
Download
Short summary
Our study uses a statistical boosting model for estimating low flows on a monthly basis, which can be applied to estimate low flows at sites without measurements. We use an extensive dataset of 260 stream gauges in Austria for model development. As we are specifically interested in low-flow events, our method gives specific weight to such events. We found that our method can considerably improve the predictions of low-flow events and yields accurate estimates of the seasonal low-flow variation.