Preprints
https://doi.org/10.5194/nhess-2021-223
https://doi.org/10.5194/nhess-2021-223

  16 Aug 2021

16 Aug 2021

Review status: this preprint is currently under review for the journal NHESS.

Forecasting Vegetation Condition with a Bayesian Auto-regressive Distributed Lags (BARDL) Model

Edward E. Salakpi1, Peter D. Hurley1,2, James M. Muthoka3, Adam B. Barrett4, Andrew Bowell1,2, Seb Oliver1,2, and Pedram Rowhani3 Edward E. Salakpi et al.
  • 1The Data Intensive Science Centre, Department of Physics and Astronomy, University of Sussex, Brighton BN1 9QH, UK
  • 2Astronomy Centre, Department of Physics and Astronomy, University of Sussex, Brighton BN1 9QH, UK
  • 3Department of Geography, School of Global Studies, University of Sussex, Brighton BN1 9QJ, UK
  • 4Sackler Centre for Consciousness Science, Department of Informatics, University of Sussex, Brighton BN1 9QJ, UK

Abstract. Droughts form a large part of climate/weather-related disasters reported globally. In Africa, pastoralists living in the Arid and Semi-Arid Lands (ASALs) are the worse affected. Prolonged dry spells that cause vegetation stress in these regions have resulted in the loss of income and livelihoods. To curb this, global initiatives like the Paris Agreement and the United Nations recognised the need to establish Early Warning Systems (EWS) to save lives and livelihoods. Existing EWS use a combination of Satellite Earth Observation (EO) based biophysical indicators like the Vegetation Condition Index (VCI) and socio-economic factors to measure and monitor droughts. Most of these EWS rely on expert knowledge in estimating upcoming drought conditions without using forecast models. Recent research has shown that the use of robust algorithms like Auto-Regression, Gaussian Processes and Artificial Neural Networks can provide very skilled models for forecasting vegetation condition at short to medium range lead times. However, to enable preparedness for early action, forecasts with a longer lead time are needed. The objective of this research work is to develop models that forecast vegetation conditions at longer lead times on the premise that vegetation condition is controlled by factors like precipitation and soil moisture. To achieve this, we used a Bayesian Auto-Regressive Distributed Lag (BARDL) modelling approach which enabled us to factor in lagged information from Precipitation and Soil moisture levels into our VCI forecast model. The results showed a ∼2-week gain in the forecast range compared to the univariate AR model used as a baseline. The R2 scores for the Bayesian ARDL model were 0.94, 0.85 and 0.74, compared to the AR model's R2 of 0.88, 0.77 and 0.65 for 6, 8 and 10 weeks lead time respectively.

Edward E. Salakpi et al.

Status: open (until 10 Oct 2021)

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Edward E. Salakpi et al.

Edward E. Salakpi et al.

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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 ten weeks lead time with an accuracy of 74 %. Our model can be adopted by policy makers and relief agencies for drought early warning and early action.
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