Acta Geographica Sinica ›› 2021, Vol. 76 ›› Issue (3): 525-538.doi: 10.11821/dlxb202103003

• Climate Change and Land Surface Processes • Previous Articles     Next Articles

Prediction of agricultural drought in China based on Meta-Gaussian model

WU Haijiang1,2(), SU Xiaoling1,2(), ZHANG Gengxi1   

  1. 1. College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China
    2. Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Yangling 712100, Shaanxi, China
  • Received:2020-02-17 Revised:2020-10-28 Online:2021-03-25 Published:2021-05-25
  • Contact: SU Xiaoling E-mail:haijiangwu@nwafu.edu.cn;xiaolingsu@nwafu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(51879222)

Abstract:

It is predicted that many regions will witness higher frequencies of drought events under global climate change, which could pose a threat to crop yield and water security. Therefore, the development of efficient and reliable methods for agricultural drought prediction is crucial. This study used the Standardized Precipitation Index (SPI) based on monthly precipitation at a 6-month time scale as an indicator of meteorological drought. The Joint Standardized Soil Moisture Index (JSSI) was used to assess the comprehensive situation of agricultural drought and was derived by combining the Standardized Soil Moisture Index (SSI) over 1-, 3-, 6-, 9-, and 12-month time scales based on monthly root zone soil moisture. Using the antecedent SPI and the persistent JSSI as predictors, the Meta-Gaussian (MG) model was applied to predict agricultural drought in China from June to August in 1961-2015. The Brier Skill Score (BSS) and Nash-Sutcliffe Efficiency Coefficient (NSE) were adopted for the evaluation of the prediction performance of the MG model. The results showed that the JSSI was capable of capturing both emerging and prolonged agricultural droughts in a timely manner, which is significant for agricultural drought monitoring. The spatial distribution of predictions of severe agricultural droughts with the 1- to 3-month lead by the JSSI for June to August in 2010 and 2014 resembled the corresponding observations for most parts of China. Moreover, the areas with a predicted higher probability of JSSI falling below -0.5 corresponded well with areas that experienced agricultural drought according to observed data (JSSI < -0.5). The BSS and NSE results confirmed that the MG model was able to provide reliable predictions of agricultural drought for June to August in most parts of China. The prediction of JSSI from June to August by the MG model with the 1-month lead showed that the proportions of the total area with BSS ≥ 0.5 were 0.714, 0.642, and 0.640, respectively, whereas the proportions of the total area with NSE ≥ 0.5 were 0.903, 0.829, and 0.837, respectively. However, the MG model performed poorly in desert areas, including southern Xinjiang, western Qinghai and western Inner Mongolia, which may have been due to the extremely arid conditions of these regions with soil water mostly related to condensation water rather than rainfall. The results of this study can provide scientific basis for agricultural drought monitoring, early warning, and decision-making in China.

Key words: agricultural drought, drought prediction, prediction probability, Meta-Gaussian model