Articles | Volume 22, issue 8
https://doi.org/10.5194/nhess-22-2703-2022
https://doi.org/10.5194/nhess-22-2703-2022
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

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Cited articles

Adede, C., Oboko, R., Wagacha, P. W., and Atzberger, C.: A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya's Operational Drought Monitoring, Remote Sens., 11, 1099, https://doi.org/10.3390/rs11091099, 2019. a
AghaKouchak, A.: A baseline probabilistic drought forecasting framework using standardized soil moisture index: Application to the 2012 United States drought, Hydrol. Earth Syst. Sci., 18, 2485–2492, https://doi.org/10.5194/hess-18-2485-2014, 2014. a, b
Akaike, H.: Information theory and an extension of the maximum likelihood principle, in: Selected papers of hirotugu akaike, Springer, New York, NY, 199–213, https://doi.org/10.1007/978-1-4612-1694-0_15, 1998. a
Asaad, A.-A. B. and Magadia, J. C.: Stochastic Gradient Hamiltonian Monte Carlo on Bayesian Time Series Modeling, in: 14th National Convention on Statistics Crowne Plaza Manila Galleria, 1–3 October 2019, Ortigas Center, Quezon City, 2019. a
Barrett, A. B., Duivenvoorden, S., Salakpi, E. E., Muthoka, J. M., Mwangi, J., Oliver, S., and Rowhani, P.: Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya, Remote Sens. Environ., 248, 111886, https://doi.org/10.1016/j.rse.2020.111886, 2020. a, b, c, d, e
Short summary
The devastating effects of recurring drought conditions are mostly felt by pastoralists that rely on grass and shrubs as fodder for their animals. Using historical information from precipitation, soil moisture, and vegetation health data, we developed a model that can forecast vegetation condition and the probability of drought occurrence up till a 10-week lead time with an accuracy of 74 %. Our model can be adopted by policymakers and relief agencies for drought early warning and early action.
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