Articles | Volume 22, issue 8
https://doi.org/10.5194/nhess-22-2725-2022
https://doi.org/10.5194/nhess-22-2725-2022
Research article
 | 
23 Aug 2022
Research article |  | 23 Aug 2022

A dynamic hierarchical Bayesian approach for forecasting vegetation condition

Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Andrew Bowell, Seb Oliver, and Pedram Rowhani

Viewed

Total article views: 1,574 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,082 442 50 1,574 39 39
  • HTML: 1,082
  • PDF: 442
  • XML: 50
  • Total: 1,574
  • BibTeX: 39
  • EndNote: 39
Views and downloads (calculated since 07 Dec 2021)
Cumulative views and downloads (calculated since 07 Dec 2021)

Viewed (geographical distribution)

Total article views: 1,574 (including HTML, PDF, and XML) Thereof 1,390 with geography defined and 184 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 19 Jun 2024
Short summary
The impact of drought may vary in a given region depending on whether it is dominated by trees, grasslands, or croplands. The differences in impact can also be the agro-ecological zones within the region. This paper proposes a hierarchical Bayesian model (HBM) for forecasting vegetation condition in spatially diverse areas. Compared to a non-hierarchical model, the HBM proved to be a more natural method for forecasting drought in areas with different land covers and agro-ecological zones.
Altmetrics
Final-revised paper
Preprint