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.
Highlights
Drought is a naturally occurring phenomenon that affects the food security of approximately 55 million people annually and can severely impact a country’s economy (Deleersnyder, 2018; Nicolai-Shaw et al, 2017)
Prolonged meteorological drought event mainly leads to a significant reduction in the amount of soil moisture required for vegetation growth, resulting 20 in an agricultural drought (Heim, 2002; Boken et al, 2005)
Our dynamic Hierarchical Bayesian Model (HBM) for forecasting VCI3M were tested on datasets based on their Agro-Ecological Zones (AEZ) and vegetation land covers
Summary
Drought is a naturally occurring phenomenon that affects the food security of approximately 55 million people annually and can severely impact a country’s economy (Deleersnyder, 2018; Nicolai-Shaw et al, 2017). Prolonged meteorological drought event mainly leads to a significant reduction in the amount of soil moisture required for vegetation growth, resulting 20 in an agricultural drought (Heim, 2002; Boken et al, 2005). Agricultural drought events are considered a physical manifestation of meteorological drought (Boken et al, 2005). Agricultural drought, which is the focus of this paper, is the most complex amongst the drought categories (Boken et al, 2005). Its onset can be slow and can occur in vast areas with varying spatial impact (Boken et al, 2005).
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