A dynamic hierarchical Bayesian approach  for forecasting vegetation condition

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

doi:https://doi.org/10.5194/nhess-22-2725-2022

Articles | Volume 22, issue 8

https://doi.org/10.5194/nhess-22-2725-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-22-2725-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 8

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

Data sets

MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted Ref Daily L3 Global - 500 m V006 C. Schaaf and Z. Wang https://doi.org/10.5067/MODIS/MCD43A4.006

Model code and software

edd3x/Hierarchical-Bayesian-ARDL: Data and code (scripts) for forecasting Vegetation Condition (Drought) with a Hierarchical Bayesian Model (v1.0) E. E. Salakpi and A. Bowel https://doi.org/10.5281/zenodo.7005178

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.