Forecasting vegetation condition with a Bayesian  auto-regressive distributed lags (BARDL) model

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

doi:https://doi.org/10.5194/nhess-22-2703-2022

Articles | Volume 22, issue 8

https://doi.org/10.5194/nhess-22-2703-2022

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

https://doi.org/10.5194/nhess-22-2703-2022

© 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

Forecasting vegetation condition with a Bayesian auto-regressive distributed lags (BARDL) model

Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Adam B. Barrett, 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/Bayesian-ARDL: Data and Code for Bayesian Auto-regressive Distributed lag models (v.1.0) E. E. Salakpi, A. B. Barrett, and A. Bowell https://doi.org/10.5281/zenodo.7005168

Short summary

The devastating effects of recurring drought conditions are mostly felt by pastoralists that...