Analysis of rainfall and large-scale predictors using a stochastic model and artificial neural network for hydrological applications in southern Africa

Rainfall is a major requirement for many water resources applications, including food production and security. Understanding the main drivers of rainfall and its variability in semi-arid areas is a key to unlocking the complex rainfall processes influencing the translation of rainfall into runoff. I...

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Bibliographic Details
Published in:Hydrological sciences journal 2015-11, Vol.60 (11), p.1943-1955
Main Authors: Kenabatho, P.K., Parida, B.P., Moalafhi, D.B., Segosebe, T.
Format: Article
Language:English
Subjects:
Afrique du Sud
artificial neural networks
generalized linear models
modèles linéaires généralisés
modèles stochastiques
pluie
rainfall
réseaux de neurones artificiels
southern Africa
stochastic models
teleconnections
téléconnexions
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Summary:Rainfall is a major requirement for many water resources applications, including food production and security. Understanding the main drivers of rainfall and its variability in semi-arid areas is a key to unlocking the complex rainfall processes influencing the translation of rainfall into runoff. In recent studies, temperature and humidity were found to be among rainfall predictors in Botswana and South African catchments when using complex rainfall models based on the generalized linear models (GLMs). In this study, we explore the use of other less complex models such as artificial neural networks (ANNs), and Multiplicative Autoregressive Integrated Moving Average (MARIMA) (a) to further investigate the association between rainfall and large-scale rainfall predictors in Botswana, and (b) to forecast these predictors to simulate rainfall at shorter future time scales (October-December) for policy applications. The results indicate that ANN yields better estimates of forecasted temperatures and rainfall than MARIMA. Editor D. Koutsoyiannis; Guest editor D. Hughes
ISSN:0262-6667
2150-3435
DOI:10.1080/02626667.2015.1040021