Preprints
https://doi.org/10.5194/nhess-2021-290
https://doi.org/10.5194/nhess-2021-290
 
07 Dec 2021
07 Dec 2021
Status: this preprint is currently under review for the journal NHESS.

A Dynamic Hierarchical Bayesian Approach for Forecasting Vegetation Condition

Edward E. Salakpi1, Peter D. Hurley1,2, James M. Muthoka3, 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

Abstract. Agricultural drought, which occurs due to a significant reduction in the moisture required for vegetation growth, is the most complex amongst all drought categories. The onset of agriculture drought is slow and can occur over vast areas with varying spatial effects, differing in areas with a particular vegetation land cover or specific agro-ecological sub-regions. These spatial variations imply that monitoring and forecasting agricultural drought require complex models that consider the spatial variations in a given region of interest. Hierarchical Bayesian Models are suited for modelling such complex systems. Using partially pooled data with sub-groups that characterise spatial differences, these models can capture the sub-group variation while allowing flexibility and information sharing between these sub-groups. This paper's objective was to improve the accuracy and precision of agricultural drought forecast in spatially diverse regions with a Hierarchical Bayesian Model. Results showed that the Hierarchical Bayesian Model was better at capturing the variability for the different agro-ecological zones and vegetation land covers compared to a regular Bayesian Auto-Regression Distributed Lags model. The forecasted vegetation condition and associated drought probabilities were more accurate and precise with the Hierarchical Bayesian Model at 4 to 10 weeks lead times. Forecasts from the hierarchical model exhibited higher hit rates with a low probability of false alarms for drought events in semi-arid and arid zones. The Hierarchical Bayesian Model also showed good transferable forecast skills over counties not included in the training data.

Edward E. Salakpi et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-290', Anonymous Referee #1, 22 Dec 2021
    • AC1: 'Reply on RC1', Edward Salakpi, 08 Apr 2022
  • RC2: 'Comment on nhess-2021-290', Anonymous Referee #2, 26 Feb 2022
    • AC2: 'Reply on RC2', Edward Salakpi, 08 Apr 2022

Edward E. Salakpi et al.

Edward E. Salakpi et al.

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Short summary
The impact of drought may vary in a given region depending on whether they are dominated by trees, grasslands or croplands. The differences in impact can also be the agro-ecological zones within the region. This paper proposed 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 coves and agro-ecological zones.
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