Drought forecasting: A review of modelling approaches 2007–2017

Abstract Droughts are prolonged precipitation-deficient periods, resulting in inadequate water availability and adverse repercussions to crops, animals and humans. Drought forecasting is vital to water resources planning and management in minimizing the negative consequences. Many models have been developed for this purpose and, indeed, it would be a long process for researchers to select the best suited model for their research. A timely, thorough and informative overview of the models' concepts and historical applications would be helpful in preventing researchers from overlooking the potential selection of models and saving them considerable amounts of time on the problem. Thus, this paper aims to review drought forecasting approaches including their input requirements and performance measures, for 2007–2017. The models are categorized according to their respective mechanism: regression analysis, stochastic, probabilistic, artificial intelligence based, hybrids and dynamic modelling. Details of the selected papers, including modelling approaches, authors, year of publication, methods, input variables, evaluation criteria, time scale and type of drought are tabulated for ease of reference. The basic concepts of each approach with key parameters are explained, along with the historical applications, benefits and limitations of the models. Finally, future outlooks and potential modelling techniques are furnished for continuing drought research.