Abstract
Forecasting of droughts is essential for developing measures for mitigation of drought hazards and for reducing drought-induced loss. In this study, droughts were characterized by the standardized precipitation-evapotranspiration index with a time scale of 3 months. Copula-based probabilistic forecasting models were developed to predict drought occurrences. Results indicated higher probability of occurrence of seasonal droughts after the occurrence of more severe seasonal droughts, and extreme drought in winter tended to persist with higher probability till spring, whereas extreme drought in autumn might not probably last to winter. Furthermore, results indicated high probability of occurrence of droughts in southeast parts of the Pearl River basin during spring to winter. Thus, droughts in the Pearl River basin are subject to lengthening duration, particularly in the southeastern part of the basin. It should be noted here that the southeastern part is densely populated with a high degree of socioeconomic development. Thus, higher probability of droughts in the southeastern part should attract considerable concern. Higher drought risk was also identified in the western part of the basin. Results of this study provide a theoretical framework for water resources management and conservation of eco-environment in the Pearl River basin in a changing climate, and may serve as a reference for evaluation of drought risk in other regions of the world.
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Acknowledgments
This work is financially supported by National Science Foundation for Distinguished Young Scholars of China (Grant No.: 51425903), the Project supported by the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (Grant No. 51210013), and is fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK441313).
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Chen, Y.D., Zhang, Q., Xiao, M. et al. Probabilistic forecasting of seasonal droughts in the Pearl River basin, China. Stoch Environ Res Risk Assess 30, 2031–2040 (2016). https://doi.org/10.1007/s00477-015-1174-6
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DOI: https://doi.org/10.1007/s00477-015-1174-6