IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v255y2021ics0378377421002936.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377421002936
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2021.107028?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Samantaray, Alok Kumar & Ramadas, Meenu & Panda, Rabindra Kumar, 2022. "Changes in drought characteristics based on rainfall pattern drought index and the CMIP6 multi-model ensemble," Agricultural Water Management, Elsevier, vol. 266(C).
    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. 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).
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Samantaray, Alok Kumar & Ramadas, Meenu & Panda, Rabindra Kumar, 2022. "Changes in drought characteristics based on rainfall pattern drought index and the CMIP6 multi-model ensemble," Agricultural Water Management, Elsevier, vol. 266(C).
    2. Zhang, Yu & Hao, Zengchao & Feng, Sifang & Zhang, Xuan & Hao, Fanghua, 2022. "Changes and driving factors of compound agricultural droughts and hot events in eastern China," Agricultural Water Management, Elsevier, vol. 263(C).
    3. Zhang, Yitong & Hao, Zengchao & Zhang, Yu, 2023. "Agricultural risk assessment of compound dry and hot events in China," Agricultural Water Management, Elsevier, vol. 277(C).
    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. Chujie Gao & Haishan Chen & Shanlei Sun & Victor Ongoma & Wenjian Hua & Hedi Ma & Bei Xu & Yang Li, 2018. "A potential predictor of multi-season droughts in Southwest China: soil moisture and its memory," 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. 91(2), pages 553-566, March.
    6. Wang, Fei & Lai, Hexin & Li, Yanbin & Feng, Kai & Zhang, Zezhong & Tian, Qingqing & Zhu, Xiaomeng & Yang, Haibo, 2022. "Dynamic variation of meteorological drought and its relationships with agricultural drought across China," Agricultural Water Management, Elsevier, vol. 261(C).
    7. Tao He & Wenya Zhang & Hanwen Zhang & Jinliang Sheng, 2023. "Estimation of Manure Emissions Issued from Different Chinese Livestock Species: Potential of Future Production," Agriculture, MDPI, vol. 13(11), pages 1-17, November.
    8. 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).
    9. Manman Zhang & Dang Luo & Yongqiang Su, 2022. "Drought monitoring and agricultural drought loss risk assessment based on multisource information fusion," 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. 111(1), pages 775-801, March.
    10. Chong Du & Jiashuo Chen & Tangzhe Nie & Changlei Dai, 2022. "Spatial–temporal changes in meteorological and agricultural droughts in Northeast China: change patterns, response relationships and causes," 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. 110(1), pages 155-173, January.
    11. Xu, Yang & Zhang, Xuan & Hao, Zengchao & Hao, Fanghua & Li, Chong, 2021. "Projections of future meteorological droughts in China under CMIP6 from a three‐dimensional perspective," Agricultural Water Management, Elsevier, vol. 252(C).
    12. Ding, Yibo & Gong, Xinglong & Xing, Zhenxiang & Cai, Huanjie & Zhou, Zhaoqiang & Zhang, Doudou & Sun, Peng & Shi, Haiyun, 2021. "Attribution of meteorological, hydrological and agricultural drought propagation in different climatic regions of China," Agricultural Water Management, Elsevier, vol. 255(C).
    13. Li, Pei & Huang, Qiang & Huang, Shengzhi & Leng, Guoyong & Peng, Jian & Wang, Hao & Zheng, Xudong & Li, Yifei & Fang, Wei, 2022. "Various maize yield losses and their dynamics triggered by drought thresholds based on Copula-Bayesian conditional probabilities," Agricultural Water Management, Elsevier, vol. 261(C).
    14. Yanqun Ren & Jinping Liu & Patrick Willems & Tie Liu & Quoc Bao Pham, 2023. "Detection and Assessment of Changing Drought Events in China in the Context of Climate Change Based on the Intensity–Area–Duration Algorithm," Land, MDPI, vol. 12(10), pages 1-18, September.
    15. Mondol, Md Anarul Haque & Zhu, Xuan & Dunkerley, David & Henley, Benjamin J., 2021. "Observed meteorological drought trends in Bangladesh identified with the Effective Drought Index (EDI)," Agricultural Water Management, Elsevier, vol. 255(C).
    16. Omidreza Mikaili & Majid Rahimzadegan, 2022. "Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province, Iran)," 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. 111(3), pages 2511-2529, April.
    17. Chhanda Ruj & Aloke Majumdar & Somnath Ghosal, 2022. "Political ecology and hydrosocial relation: a study on drought and associated migration in a semi-arid district of West Bengal, India," Letters in Spatial and Resource Sciences, Springer, vol. 15(3), pages 709-734, December.
    18. Tuan Minh Cao & Sang Hyeon Lee & Ji Yong Lee, 2023. "The Impact of Natural Disasters and Pest Infestations on Technical Efficiency in Rice Production: A Study in Vietnam," Sustainability, MDPI, vol. 15(15), pages 1-16, July.
    19. Araneda-Cabrera, Ronnie J. & Bermúdez, María & Puertas, Jerónimo, 2021. "Assessment of the performance of drought indices for explaining crop yield variability at the national scale: Methodological framework and application to Mozambique," Agricultural Water Management, Elsevier, vol. 246(C).
    20. Wei Wei & Jiping Wang & Libang Ma & Xufeng Wang & Binbin Xie & Junju Zhou & Haoyan Zhang, 2024. "Global Drought-Wetness Conditions Monitoring Based on Multi-Source Remote Sensing Data," Land, MDPI, vol. 13(1), pages 1-19, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:255:y:2021:i:c:s0378377421002936. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.