Skip to main content

Advertisement

Log in

Effects of drought and flood on crop production in China across 1949–2015: spatial heterogeneity analysis with Bayesian hierarchical modeling

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

China is an agricultural country with the largest population in the world. However, intensification of droughts and floods has substantial impacts on agricultural production. For effective agricultural disaster management, it is significant to understand and quantify the influence of droughts and floods on crop production. Compared with droughts, the influence of floods on crop production and a comprehensive evaluation of effects of droughts and floods are given relatively less attention. The impact of droughts and floods on crop production is therefore investigated in this study, considering spatial heterogeneity with disaster and yield datasets for 1949–2015 in China mainland. The empirical relationships between drought and flood intensity and yield fluctuation for grain, rice, wheat, maize and soybean are identified using a Bayesian hierarchical model. They are then used to explore what social-economic factors influenced the grain sensitivity to droughts and floods by the Pearson’s coefficient and locally weighted regression (LOSEE) plots. The modeling results indicate that: (a) droughts significantly reduce grain yields in 28 of 31 provinces and obvious spatial variability in drought sensitivity exists, with Loess Plateau having highest probability of crop failure caused by droughts; (b) floods significantly reduce grain yield in 20 provinces, while show positive effect in the northwestern and southwestern China; (c) the spatial patterns of influence direction of droughts and floods on rice, maize and soybean are consistent with the grain’s results; and (d) promoting capital investments and improving access to technical inputs (fertilizer, pesticide, and irrigation) can help effectively buffer grain yield lose from droughts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Al-Qudah KA (2011) Floods as water resource and as a hazard in arid regions: a case study in Southern Jordan. Jordan J Civ Eng 5(1):148–161

    Google Scholar 

  • Antwi-Agyei P, Fraser EDG, Dougill AJ, Stringer LC, Simelton E (2012) Mapping the vulnerability of crop production to drought in Ghana using rainfall, yield and socioeconomic data. Appl Geogr 32(32):324–334

    Article  Google Scholar 

  • Bates D, Machler M, Bolker BM, Walker SC (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48

    Article  Google Scholar 

  • Brémond P, Grelot F, Agenais AL (2013) Review article: economic evaluation of flood damage to agriculture-review and analysis of existing methods. Nat Hazards Earth Syst Sci 13(10):2493–2512

    Article  Google Scholar 

  • Brooks N, Adger WN, Kelly PM (2005) The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation. Glob Environ Change 15(2):151–163

    Article  Google Scholar 

  • Chakrabarti S, Bongiovanni T, Judge J, Zotarelli L (2014) Assimilation of SMOS soil moisture for quantifying drought impacts on crop yield in agricultural regions. IEEE J Sel Top Appl Earth Obs Remote Sens 7(9):3867–3879

    Article  Google Scholar 

  • Chen Y, Li X (2013) Spatial-temporal characteristics and influencing factors of grain yield change in China. Trans Chin Soc Agric Eng 29(20):1–10 (in Chinese)

    Google Scholar 

  • Chen L, Wang G, Zhong Y, Shen Z (2016a) Evaluating the impacts of soil data on hydrological and nonpoint source pollution prediction. Sci Total Environ 563:19–28

    Article  Google Scholar 

  • Chen T, Xia G, Liu T, Chen W, Chi D (2016b) Assessment of drought impact on main cereal crops using a standardized precipitation evapotranspiration index in Liaoning Province, China. Sustainability 8(10):1069

    Article  Google Scholar 

  • Clark JS (2005) Why environmental scientists are becoming Bayesians. Ecol Lett 8(1):2–14

    Article  Google Scholar 

  • Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat As 74(368):829–836

    Article  Google Scholar 

  • Deng J (1982) Some problems on the comprehensive agricultural regionalization of China. Geogr Res 1(1):9–18 (in Chinese)

    Google Scholar 

  • Dixon B (2005) Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. J Hydrol 309(1–4):17–38

    Article  Google Scholar 

  • Duan W, He B, Nover D, Fan J, Yang G, Chen W et al (2016) Floods and associated socioeconomic damages in China over the last century. Nat Hazards 82(1):401–413

    Article  Google Scholar 

  • Dunson DB (2009) Bayesian nonparametric hierarchical modeling. Biom J 51(2):273–284

    Article  Google Scholar 

  • Ellison AM (2004) Bayesian inference in ecology. Ecol Lett 7(6):509–520

    Article  Google Scholar 

  • Eriyagama N, Muthuwatta L, Thilakarathne M (2014) Minimizing flood damage and augmenting dry season water availability: prospects for floodwater harvesting and underground storage in Sri Lanka. In: Proceedings of the disaster management conference, pp 379–381

  • Farhangfar S, Bannayan M, Khazaei HR, Baygi MM (2015) Vulnerability assessment of wheat and maize production affected by drought and climate change. Int J Disaster Risk Reduct 13:37–51

    Article  Google Scholar 

  • Fraser EDG (2007) Travelling in antique lands: using past famines to develop an adaptability/resilience framework to identify food systems vulnerable to climate change. Clim Change 83(4):495–514

    Article  Google Scholar 

  • Fraser EDG, Dougill A, Hubacek K, Quinn C, Sendzimir J, Termansen M (2011) Assessing vulnerability to climate change in dryland livelihood systems: conceptual challenges and interdisciplinary solutions. Ecol Soc 16(3):3

    Article  Google Scholar 

  • Gelman A (2006) Multilevel (hierarchical) modeling: what it can and cannot do. Technometrics 48(3):432–435

    Article  Google Scholar 

  • Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models, vol 1. Cambridge University Press, New York

    Google Scholar 

  • Gu H, Liu X, Liu Z (2014) Characteristics of major drought disaster in China, distribution and its formation mechanism research. Southwest China J Agric Sci 27(1):369–373 (in Chinese)

    Google Scholar 

  • Guo XY, Guo JW, Chen XM, Wang JL, Li S (2011) Temporal and spatial distribution of drought-flood hazards in Gansu Province and its relationship with regional grain output. J Arid Land Resour Environ 25(6):132–137 (in Chinese)

    Google Scholar 

  • Guo E, Liu X, Zhang J, Wang Y, Wang C, Wang R, Li D (2017) Assessing spatiotemporal variation of drought and its impact on maize yield in Northeast China. J Hydrol 553:231–247

    Article  Google Scholar 

  • He B, Wu J, Lü A, Cui X, Lei Z, Ming L et al (2013) Quantitative assessment and spatial characteristic analysis of agricultural drought risk in China. Nat Hazards 66(2):155–166

    Article  Google Scholar 

  • Hlavinka P, Trnka M, Semerádová D, Dubrovský M, Žalud Z, Možný M (2009) Effect of drought on yield variability of key crops in Czech Republic. Agric For Meteorol 149(3–4):431–442

    Article  Google Scholar 

  • Huang F, Li B (2010) Assessing grain crop water productivity of China using a hydro-model-coupled-statistics approach. Part II: application in breadbasket basins of China. Agric Water Manag 97(9):1259–1268

    Article  Google Scholar 

  • IPCC (2001) Glossary, fourth assessment report, working group 2. Intergovernmental panel on climate change. Cambridge University Press, Cambridge and New York

  • Jiang G, Yu F, Zhao Y (2012) An analysis of vulnerability to agricultural drought in China using the expand grey relation analysis method. Procedia Eng 28:670–676

    Article  Google Scholar 

  • Kent RJ, Johnson DE (2001) Influence of flood depth and duration on growth of lowland rice weeds, Cote d’Ivoire. Crop Prot 20(8):691–694

    Article  Google Scholar 

  • Kotera A, Nagano T, Hanittinan P, Koontanakulvong S (2016) Assessing the degree of flood damage to rice crops in the Chao Phraya delta, Thailand, using MODIS satellite imaging. Paddy Water Environ, 14(1):1–10

    Article  Google Scholar 

  • Lesk C, Rowhani P, Ramankutty N (2016) Influence of extreme weather disasters on global crop production. Nature 529(7584):84–87

    Article  Google Scholar 

  • Li S, Du X, Tong Y, Chi Y, Lu Y, Wang Z (2011) Experiment study on promotion techniques for rain & flood water harvesting through underground storage. Hydrogeol Eng Geol 38(5):13–19

    Google Scholar 

  • Li S, Tompkins AM, Lin E, Ju H (2016) Simulating the impact of flooding on wheat yield—case study in East China. Agric For Meteorol 216(34):221–231

    Article  Google Scholar 

  • Liu X, Zhang J, Ma D, Bao Y, Tong Z, Liu X (2013) Dynamic risk assessment of drought disaster for maize based on integrating multi-sources data in the region of the northwest of Liaoning Province, China. Nat Hazards 65(3):1393–1409

    Article  Google Scholar 

  • Liu Y, Liu B, Yang X, Bai W, Wang J (2015) Relationships between drought disasters and crop production during ENSO episodes across the North China Plain. Reg Environ Change 15(8):1689–1701

    Article  Google Scholar 

  • Lobell DB, Hammer GL, Chenu K, Zheng B, Mclean G, Chapman SC (2015) The shifting influence of drought and heat stress for crops in northeast Australia. Glob Change Biol 21(11):4115

    Article  Google Scholar 

  • Lu J, Carbone GJ, Gao P (2017) Detrending crop yield data for spatial visualization of drought impacts in the United States, 1895–2014. Agric For Meteorol 237:196–208

    Article  Google Scholar 

  • Ming B, Guo YQ, Tao HB, Liu GZ, Shao-Kun LI, Wang P (2015) SPEIPM-based research on drought impact on maize yield in North China Plain. J Integr Agric 14(4):660–669

    Article  Google Scholar 

  • Pantaleoni E, Engel BA, Johannsen CJ (2007) Identifying agricultural flood damage using Landsat imagery. Precision Agric 8(1):27–36

    Article  Google Scholar 

  • Pavelic P, Srisuk K, Saraphirom P, Nadee S, Pholkern K, Chusanathas S et al (2012) Balancing-out floods and droughts: opportunities to utilize floodwater harvesting and groundwater storage for agricultural development in Thailand. J Hydrol 470–471(4):55–64

    Article  Google Scholar 

  • Peng F, Jacobs RA, Tanner MA (1996) Bayesian inference in mixtures-of-experts and hierarchical mixtures-of-experts models with an application to speech recognition. J Am Stat As 91(435):953–960

    Article  Google Scholar 

  • Piao S, Ciais P, Huang Y, Shen Z, Peng S, Li J et al (2010) The impacts of climate change on water resources and agriculture in China. Nature 467(7311):43

    Article  Google Scholar 

  • Potop V, Možný M, Soukup J (2012) Drought at various time scales in the lowland regions and their impact on vegetable crops in the Czech Republic. Agric For Meteorol 156(8):121–133

    Article  Google Scholar 

  • Potopová V, Boroneanţ C, Boincean B, Soukup J (2015) Impact of agricultural drought on main crop yields in the Republic of Moldova. Int J Climatol 36(4):2063–2082

    Article  Google Scholar 

  • Qian SS, Cuffney TF, Alameddine I, McMahon G, Reckhow KH (2010) On the application of multilevel modeling in environmental and ecological studies. Ecology 91:355–361

    Article  Google Scholar 

  • Qian SS, Chaffin JD, DuFour MR, Sherman JJ, Goinick PC, Collier CD et al (2015) Quantifying and reducing uncertainty in estimated microcystin concentrations from the ELISA method. Environ Sci Technol 49:14221–14229

    Article  Google Scholar 

  • Qin Z, Tang H, Li W, Zhang H, Zhao S, Wang Q (2014) Modelling impact of agro-drought on grain production in China. Int J Disaster Risk Reduct 7:109–121

    Article  Google Scholar 

  • Rouder JN, Lu J (2005) An introduction to Bayesian hierarchical models with an application in the theory of signal detection. Psychon Bull Rev 12(4):573–604

    Article  Google Scholar 

  • Saher FN, Nasly MA, Kadir TAA, Yahaya NKEM, Wan Ishak WMF (2015) Managing flood water of hill torrents as potential source for irrigation. J Flood Risk Manag 8(1):87–95

    Article  Google Scholar 

  • Samantaray D, Chatterjee C, Singh R, Gupta PK, Panigrahy S (2015) Flood risk modeling for optimal rice planning for delta region of Mahanadi river basin in India. Nat Hazards 76(1):347–372

    Article  Google Scholar 

  • Shentsis I, Rosenthal E (2003) Recharge of aquifers by flood events in an arid region. Hydrol Process 17(17):695–712

    Article  Google Scholar 

  • Simelton E, Fraser EDG, Termansen M, Benton TG, Gosling SN, South A et al (2012) The socioeconomics of food crop production and climate change vulnerability: a global scale quantitative analysis of how grain crops are sensitive to drought. Food Secur 4(2):163–179

    Article  Google Scholar 

  • Stangl DK (1995) Prediction and decision making using Bayesian hierarchical models. Stat Med 14(20):2173–2190

    Article  Google Scholar 

  • Sun J, Liu Y (2014) Responses of tree-ring growth and crop yield to drought indices in the Shanxi province, North China. Int J Biometeorol 58(7):1521–1530

    Article  Google Scholar 

  • Thornton PK, Ericksen PJ, Herrero M, Challinor AJ (2014) Climate variability and vulnerability to climate change: a review. Glob Change Biol 20(11):3313–3328

    Article  Google Scholar 

  • Troy TJ, Kipgen C, Pal I (2015) The impact of climate extremes and irrigation on US crop yields. Environ Res Lett 10(5):054013

    Article  Google Scholar 

  • Turner BL, Kasperson RE, Matson PA, McCarthy JJ, Corell RW, Christensen L, Polsky C et al (2003) A framework for vulnerability analysis in sustainability science. Proc Natl Acad Sci 100(14):8074–8079

    Article  Google Scholar 

  • Wang P, Shi PJ (2007) Comprehensive regionalization of agricultural natural disaster in China. J Nat Disasters 9(4):16–23 (in Chinese)

    Google Scholar 

  • Wikle CK (2003) Hierarchical Bayesian models for predicting the spread of ecological processes. Ecology 84(6):1382–1394

    Article  Google Scholar 

  • Wu D, Yan DH, Yang GY, Wang XG, Xiao WH, Zhang HT (2013) Assessment on agricultural drought vulnerability in the yellow river basin based on a fuzzy clustering iterative model. Nat Hazards 67(2):919–936

    Article  Google Scholar 

  • Xia J, Ning L, Wang Q, Chen J, Wan L, Hong S (2017) Vulnerability of and risk to water resources in arid and semi-arid regions of west China under a scenario of climate change. Clim Change 144(3):1–15

    Article  Google Scholar 

  • Xiong W, Holman IP, You L, Yang J, Wu W (2014) Impacts of observed growing-season warming trends since 1980 on crop yields in China. Reg Environ Change 14(1):7–16

    Article  Google Scholar 

  • Xu X, Ge Q, Zheng J, Dai E, Zhang X, He S et al (2013) Agricultural drought risk analysis based on three main crops in prefecture-level cities in the monsoon region of east China. Nat Hazards 66(2):1257–1272

    Article  Google Scholar 

  • Yildirak K, Selcuk-Kestel AS (2015) Adjusting SPI for crop specific agricultural drought. Environ Ecol Stat 22(4):681–691

    Article  Google Scholar 

  • Yin Y, Zhang X, Lin D, Yu H, Wang J, Shi P (2014) GEPIC-V-R model: a GIS-based tool for regional crop drought risk assessment. Agric Water Manag 144(2):107–119

    Article  Google Scholar 

  • Yin XG, Jabloun M, Olesen JE, Öztürk I, Wang M, Chen F (2016) Effects of climatic factors, drought risk and irrigation requirement on maize yield in the Northeast Farming Region of China. J Agric Sci 154(7):1171–1189

    Article  Google Scholar 

  • Yuan XC, Tang BJ, Wei YM, Liang XJ, Yu H, Jin JL (2015) China’s regional drought risk under climate change: a two-stage process assessment approach. Nat Hazards 76(1):667–684

    Article  Google Scholar 

  • Zhai J, Huang J, Su B, Cao L, Wang Y, Jiang T et al (2017) Intensity–area–duration analysis of droughts in china 1960–2013. Clim Dyn 48(1–2):151–168

    Article  Google Scholar 

  • Zhang Q, Gu X, Singh VP, Kong D, Chen X (2015a) Spatiotemporal behavior of floods and droughts and their impacts on agriculture in China. Global Planet Change 131:63–72

    Article  Google Scholar 

  • Zhang Q, Sun P, Li J, Singh VP, Liu J (2015b) Spatiotemporal properties of droughts and related impacts on agriculture in Xinjiang, China. Int J Climatol 35(7):1254–1266

    Article  Google Scholar 

  • Zhang Q, Gu X, Singh VP, Liu L, Kong D (2016) Flood-induced agricultural loss across China and impacts from climate indices. Global Planet Change 139:31–43

    Article  Google Scholar 

  • Zipper SC, Qiu J, Kucharik CJ (2016) Drought effects on US maize and soybean production: spatiotemporal patterns and historical changes. Environ Res Lett 11(9):094021

    Article  Google Scholar 

Download references

Acknowledgements

This paper was supported by the National Basic Research Program of China (2015CB458900) and National Science Foundation of China (51721006). Any enquiries for access to the data referred to in this article should be directed to yongliu@pku.edu.cn.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong Liu.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 685 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, H., Liang, Z., Liu, Y. et al. Effects of drought and flood on crop production in China across 1949–2015: spatial heterogeneity analysis with Bayesian hierarchical modeling. Nat Hazards 92, 525–541 (2018). https://doi.org/10.1007/s11069-018-3216-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-018-3216-0

Keywords

Navigation