| 基于Meta-Gaussian模型的中国农业干旱预测研究 |
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| 引用本文： | 吴海江,粟晓玲,张更喜.基于Meta-Gaussian模型的中国农业干旱预测研究[J].地理学报,2021,76(3):525-538. |
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| 作者姓名： | 吴海江 粟晓玲 张更喜 |
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| 作者单位： | 1.西北农林科技大学水利与建筑工程学院,杨凌 7121002.西北农林科技大学旱区农业水土工程教育部重点实验室,杨凌 712100 |
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| 基金项目： | 国家自然科学基金项目(51879222)。 |
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| 摘 要： | 在全球气候变化背景下,干旱愈加频发,有效且可靠的农业干旱预测对于保障粮食安全和水资源安全具有重要意义。以标准化降水指数（SPI）和联合标准化土壤湿度指数（JSSI）分别表征气象干旱和农业干旱,以前期的气象干旱和农业干旱指数作为预测因子,在1~3个月预见期下基于Meta-Gaussian（MG）模型对中国1961—2015年6—8月的农业干旱进行预测,并采用Brier Skill Score（BSS）和纳什效率系数（NSE）评价MG模型的预测性能。结果表明：① 将1个月、3个月、6个月、9个月和12个月时间尺度的标准化土壤湿度指数（SSI）结合起来得到的JSSI能够对中国农业干旱的综合状况进行客观评价。② 以中国2010年和2014年遭受严重的干旱事件为例,预见期为1~3个月时,除新疆南部、青海西部以及内蒙古西部等沙漠地区外,MG模型对6—8月农业干旱预测结果的分布范围与实际干旱的分布区域较吻合,预见期越短,吻合越好。③ 预见期为1个月时,6—8月BSS ≥ 0.5的面积比例分别为0.714、0.642和0.640,NSE ≥ 0.5的面积比例分别为0.903、0.829和0.837,表明MG模型能够对中国大部分区域的农业干旱作出可靠的预测。本文结果可为中国农业干旱的监测、预警及干旱决策提供科学指导。
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| 关 键 词： | 农业干旱 干旱预测 预测概率 Meta-Gaussian模型 |
| 收稿时间： | 2020-02-17 |
| 修稿时间： | 2020-10-28 |
Prediction of agricultural drought in China based on Meta-Gaussian model |
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| WU Haijiang,SU Xiaoling,ZHANG Gengxi.Prediction of agricultural drought in China based on Meta-Gaussian model[J].Acta Geographica Sinica,2021,76(3):525-538. |
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| Authors: | WU Haijiang SU Xiaoling ZHANG Gengxi |
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| Institution: | 1. College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China2. Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Yangling 712100, Shaanxi, China |
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| 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. |
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| Keywords: | agricultural drought drought prediction prediction probability Meta-Gaussian model |
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