干旱区生态环境知识资源中心(Arid): A dynamic hierarchical Bayesian approach for forecasting vegetation condition

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DOI	10.5194/nhess-22-2725-2022
	A dynamic hierarchical Bayesian approach for forecasting vegetation condition
	Salakpi, Edward E.; Hurley, Peter D.; Muthoka, James M.; Bowell, Andrew; Oliver, Seb; Rowhani, Pedram
通讯作者	Salakpi, EE
来源期刊	NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
ISSN	1561-8633
EISSN	1684-9981
出版年	2022
卷号	22 期号:8 页码:2725-2749
英文摘要	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 subgroups 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 is to improve the accuracy and precision of agricultural drought forecasting 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-week 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.
类型	Article
语种	英语
开放获取类型	gold, Green Accepted
收录类别	SCI-E
WOS记录号	WOS:000842968100001
WOS关键词	SOIL-MOISTURE ; DROUGHT ; MACHINE ; NETWORK ; MODEL
WOS类目	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393842
推荐引用方式 GB/T 7714	Salakpi, Edward E.,Hurley, Peter D.,Muthoka, James M.,et al. A dynamic hierarchical Bayesian approach for forecasting vegetation condition[J],2022,22(8):2725-2749.
APA	Salakpi, Edward E.,Hurley, Peter D.,Muthoka, James M.,Bowell, Andrew,Oliver, Seb,&Rowhani, Pedram.(2022).A dynamic hierarchical Bayesian approach for forecasting vegetation condition.NATURAL HAZARDS AND EARTH SYSTEM SCIENCES,22(8),2725-2749.
MLA	Salakpi, Edward E.,et al."A dynamic hierarchical Bayesian approach for forecasting vegetation condition".NATURAL HAZARDS AND EARTH SYSTEM SCIENCES 22.8(2022):2725-2749.