Drought forecasting: A review of modelling approaches 2007–2017

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...

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Bibliographic Details
Published in:Journal of water and climate change 2020-09, Vol.11 (3), p.771-799
Main Authors: Fung, K. F., Huang, Y. F., Koo, C. H., Soh, Y. W.
Format: Article
Language:English
Subjects:
Artificial intelligence
Climate
Drought
Drought forecasting
Dynamic models
Forecasting
Generalized linear models
Hybrids
Hydrology
Markov analysis
Mean square errors
Modelling
Neural networks
Precipitation
Regression analysis
Resource management
Statistical analysis
Stochasticity
Stream flow
Variables
Vegetation
Water availability
Water resources
Water resources management
Water resources planning
Water shortages
Weather
Wind
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Summary: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.
ISSN:2040-2244
2408-9354
DOI:10.2166/wcc.2019.236