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Agricultural drought prediction in China based on drought propagation and large-scale drivers

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  • Zhang, Yu
  • Hao, Zengchao
  • Feng, Sifang
  • Zhang, Xuan
  • Xu, Yang
  • Hao, Fanghua

Abstract

Agricultural drought is generally defined as a deficit in soil moisture, and can affect plant growth and crop yields. Accurate prediction of agricultural drought with sufficient lead time can aid agricultural planning and reduce losses in agricultural production. In this study, the meta-Gaussian model was employed to predict agricultural drought (standardized soil moisture index, or SSI) during spring and summer in China based on the initial soil moisture conditions, antecedent meteorological drought (standardized precipitation index, or SPI) and large-scale drivers (El Niño-Southern Oscillation, or ENSO). Monthly precipitation and soil moisture data from Global Land Data Assimilation System, version 2 (GLDAS-2.0) were used to compute the meteorological and agricultural drought indicators. The conditional distribution of agricultural drought given multiple predictors was used for the statistical prediction. The autocorrelation of agricultural drought, the correlation between meteorological drought and agricultural drought, and the correlation between ENSO and agricultural drought were first evaluated to understand the predictors from soil moisture persistence, drought propagation, and large-scale drivers. We then employed the conditional distribution to predict agricultural drought over the period from 1948 to 2014, in which contributions of antecedent meteorological drought and large-scale drivers to the prediction performance were evaluated. Results showed that the prediction method performed well in semi-arid and sub-humid regions during spring, but did not perform well in humid regions during summer. In addition, the incorporation of ENSO provided useful predictability for long lead time prediction in certain regions (with significant influence of ENSO). The results obtained from this study can provide useful information for early agricultural drought warning across China.

Suggested Citation

  • Zhang, Yu & Hao, Zengchao & Feng, Sifang & Zhang, Xuan & Xu, Yang & Hao, Fanghua, 2021. "Agricultural drought prediction in China based on drought propagation and large-scale drivers," Agricultural Water Management, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:agiwat:v:255:y:2021:i:c:s0378377421002936
    DOI: 10.1016/j.agwat.2021.107028
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    1. Hong, Minki & Lee, Sang-Hyun & Lee, Seung-Jae & Choi, Jin-Yong, 2021. "Application of high-resolution meteorological data from NCAM-WRF to characterize agricultural drought in small-scale farmlands based on soil moisture deficit," Agricultural Water Management, Elsevier, vol. 243(C).
    2. Guo, Erjing & Yang, Xiaoguang & Li, Tao & Zhang, Tianyi & Wilson, Lloyed Ted & Wang, Xiaoyu & Zheng, Dongxiao & Yang, Yubin, 2021. "Does ENSO strongly affect rice yield and water application in Northeast China?," Agricultural Water Management, Elsevier, vol. 245(C).
    3. Jiajin Wang & Yaobin Meng, 2013. "An analysis of the drought in Yunnan, China, from a perspective of society drought severity," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 67(2), pages 431-458, June.
    4. Zhou, Keke & Li, Jianzhu & Zhang, Ting & Kang, Aiqing, 2021. "The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China," Agricultural Water Management, Elsevier, vol. 243(C).
    5. Huang, Shengzhi & Huang, Qiang & Chang, Jianxia & Leng, Guoyong & Xing, Li, 2015. "The response of agricultural drought to meteorological drought and the influencing factors: A case study in the Wei River Basin, China," Agricultural Water Management, Elsevier, vol. 159(C), pages 45-54.
    6. Potopová, Vera & Trnka, Miroslav & Hamouz, Pavel & Soukup, Josef & Castraveț, Tudor, 2020. "Statistical modelling of drought-related yield losses using soil moisture-vegetation remote sensing and multiscalar indices in the south-eastern Europe," Agricultural Water Management, Elsevier, vol. 236(C).
    7. Han, Zhiming & Huang, Qiang & Huang, Shengzhi & Leng, Guoyong & Bai, Qingjun & Liang, Hao & Wang, Lu & Zhao, Jing & Fang, Wei, 2021. "Spatial-temporal dynamics of agricultural drought in the Loess Plateau under a changing environment: Characteristics and potential influencing factors," Agricultural Water Management, Elsevier, vol. 244(C).
    8. Vazifehkhah, Saeed & Kahya, Ercan, 2019. "Hydrological and agricultural droughts assessment in a semi-arid basin: Inspecting the teleconnections of climate indices on a catchment scale," Agricultural Water Management, Elsevier, vol. 217(C), pages 413-425.
    9. Vergni, L. & Todisco, F. & Di Lena, B. & Mannocchi, F., 2020. "Bivariate analysis of drought duration and severity for irrigation planning," Agricultural Water Management, Elsevier, vol. 229(C).
    10. Ribeiro, Andreia F.S. & Russo, Ana & Gouveia, Célia M. & Páscoa, Patrícia, 2019. "Copula-based agricultural drought risk of rainfed cropping systems," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    11. Dai, Meng & Huang, Shengzhi & Huang, Qiang & Leng, Guoyong & Guo, Yi & Wang, Lu & Fang, Wei & Li, Pei & Zheng, Xudong, 2020. "Assessing agricultural drought risk and its dynamic evolution characteristics," Agricultural Water Management, Elsevier, vol. 231(C).
    12. Hefei Huang & Huijuan Cui & Quansheng Ge, 2021. "Assessment of potential risks induced by increasing extreme precipitation under climate change," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 2059-2079, September.
    13. Huang, Shengzhi & Wang, Lu & Wang, Hao & Huang, Qiang & Leng, Guoyong & Fang, Wei & Zhang, Ying, 2019. "Spatio-temporal characteristics of drought structure across China using an integrated drought index," Agricultural Water Management, Elsevier, vol. 218(C), pages 182-192.
    14. X. Zhang & Y. Yamaguchi, 2014. "Characterization and evaluation of MODIS-derived Drought Severity Index (DSI) for monitoring the 2009/2010 drought over southwestern China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 2129-2145, December.
    15. Alex Avilés & Rolando Célleri & Javier Paredes & Abel Solera, 2015. "Evaluation of Markov Chain Based Drought Forecasts in an Andean Regulated River Basin Using the Skill Scores RPS and GMSS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1949-1963, April.
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    2. Pan, Ying & Zhu, Yonghua & Lü, Haishen & Yagci, Ali Levent & Fu, Xiaolei & Liu, En & Xu, Haiting & Ding, Zhenzhou & Liu, Ruoyu, 2023. "Accuracy of agricultural drought indices and analysis of agricultural drought characteristics in China between 2000 and 2019," Agricultural Water Management, Elsevier, vol. 283(C).
    3. Huazhu Xue & Ruirui Zhang & Wenfei Luan & Zhanliang Yuan, 2024. "The Spatiotemporal Variations in and Propagation of Meteorological, Agricultural, and Groundwater Droughts in Henan Province, China," Agriculture, MDPI, vol. 14(10), pages 1-28, October.
    4. Li, Yifei & Huang, Shengzhi & Wang, Hanye & Zheng, Xudong & Huang, Qiang & Deng, Mingjiang & Peng, Jian, 2022. "High-resolution propagation time from meteorological to agricultural drought at multiple levels and spatiotemporal scales," Agricultural Water Management, Elsevier, vol. 262(C).
    5. Lusheng Li & Lili Zhao & Yanbin Li, 2023. "Spatiotemporal Characteristics of Meteorological and Agricultural Droughts in China: Change Patterns and Causes," Agriculture, MDPI, vol. 13(2), pages 1-16, January.
    6. Zhang, Q. & Li, Y.P. & Huang, G.H. & Wang, H. & Li, Y.F. & Shen, Z.Y., 2024. "Multivariate time series convolutional neural networks for long-term agricultural drought prediction under global warming," Agricultural Water Management, Elsevier, vol. 292(C).

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