Comparison between canonical vine copulas and a meta-Gaussian model for forecasting agricultural drought over China
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Published:2022-07-22
Issue:14
Volume:26
Page:3847-3861
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ISSN:1607-7938
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Container-title:Hydrology and Earth System Sciences
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language:en
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Short-container-title:Hydrol. Earth Syst. Sci.
Author:
Wu HaijiangORCID, Su XiaolingORCID, Singh Vijay P., Zhang Te, Qi Jixia, Huang Shengzhi
Abstract
Abstract. Agricultural drought mainly stems from reduced soil moisture and
precipitation, and it causes adverse impacts on the growth of crops and
vegetation, thereby affecting agricultural production and food security. In order to develop drought mitigation measures, reliable agricultural drought
forecasting is essential. In this study, we developed an agricultural
drought forecasting model based on canonical vine copulas in
three dimensions (3C-vine model) in which antecedent meteorological
drought and agricultural drought persistence were utilized as predictors.
Furthermore, a meta-Gaussian (MG) model was selected as a reference to
evaluate the forecast skill. The agricultural drought in China in August of 2018 was
selected as a typical case study, and the spatial patterns of 1- to 3-month
lead forecasts of agricultural drought utilizing the 3C-vine model resembled
the corresponding observations, indicating the good predictive ability of
the model. The performance metrics – the Nash–Sutcliffe efficiency (NSE), the coefficient of
determination (R2), and the root-mean-square error (RMSE) – showed that the
3C-vine model outperformed the MG model with respect to forecasting agricultural drought
in August for diverse lead times. Moreover, the 3C-vine model exhibited
excellent forecast skill with respect to capturing the extreme agricultural drought over
different selected typical regions. This study may help to guide drought
early warning, drought mitigation, and water resource scheduling.
Funder
National Natural Science Foundation of China
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
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