Spatiotemporal characteristics of droughts and floods in northeastern China and their impacts on agriculture

  • Qiang Fu
  • Zhaoqiang Zhou
  • Tianxiao Li
  • Dong Liu
  • Renjie Hou
  • Song Cui
  • Peiru Yan
Original Paper


Heilongjiang Province is a major grain production base in China, and its agricultural development plays an important role in China’s social economy. Drought and flood events are the primary disasters in Heilongjiang Province and have considerable impacts on agriculture. In this study, relatively complete monthly precipitation data from 26 meteorological stations in Heilongjiang Province during the period of 1958–2013 were analyzed using the standardized precipitation index (SPI) combined with principal component analysis, Mann–Kendall trend analysis and Morlet wavelet analysis to determine the spatial and temporal distributions of drought and flood events in this province. The results were as follows: (1) the whole of Heilongjiang exhibited an aridity trend. In northern Heilongjiang, spring and summer experienced a wetting trend, and autumn and winter experienced an aridity trend. (2) The SPI3 exhibited 8- and 16-year periodic variation characteristics in spring, 10- and 22-year periodic variation characteristics in summer, and 10- and 32-year periodic variation characteristics in autumn. In addition to the 10-year periodic variation characteristics in winter, other periodic variation characteristics were observed. (3) The increasing trend in the percentage of stations affected by flood was more obvious than that affected by drought. Therefore, Heilongjiang Province is more vulnerable to flooding. (4) The influence of drought and flood disasters in Heilongjiang Province showed a growth trend, but the flood effect was more remarkable. (5) The agricultural area affected by drought and flood disasters in Heilongjiang Province showed an increasing trend. Although there was a greater increase in flood disaster area, the main types of disasters were drought-dominated.


Standardized precipitation index M–K trend analysis Morlet wavelet Principal component analysis Affected area Destroyed area 



This research was supported by funds from the National Natural Science Foundation of China (51709044, and 51479032) and the National Key R&D Plan (2017YFC0406002).


  1. Aliaga VS, Ferrelli F, Piccolo MC (2017) Regionalization of climate over the Argentine Pampas. Int J Climatol 24(10):2438–2448Google Scholar
  2. Bordi I, Fraedrich K, Jiang J-M, Sutera A (2004) Spatio-temporal variability of dry and wet periods in eastern China. Theoret Appl Climatol 79(1–2):81–91CrossRefGoogle Scholar
  3. Carvalho MJ, Melo-Gonçalves P, Teixeira JC, Rocha A (2016) Regionalization of Europe based on a K-means cluster analysis of the climate change of temperatures and precipitation. Phys Chem Earth Parts A/b/c 94:22–28CrossRefGoogle Scholar
  4. Emmer A, Merkl S, Mergili M (2015) Spatiotemporal patterns of high-mountain lakes and related hazards in western Austria. Geomorphology 246(246):602–616CrossRefGoogle Scholar
  5. Farge M (2003) Wavelet transforms and their applications to turbulence. Phys Today 56(4):68CrossRefGoogle Scholar
  6. Fu Q, Li TX, Li TN, Li H (2016) Temporal-spatial evolution patterns of the annual precipitation considering the climate change conditions in the Sanjiang Plain. J Water Clim Change 7(1):198–211. Google Scholar
  7. Gao J, Liu Y (2011) Climate warming and land use change in Heilongjiang Province, Northeast China. Appl Geogr 31(2):476–482CrossRefGoogle Scholar
  8. Gao C, Zhang Z, Zhai J, Liu Q, Yao M (2015) Research on meteorological thresholds of drought and flood disaster: a case study in the Huai River Basin, China. Stoch Env Res Risk Assess 29(1):157–167CrossRefGoogle Scholar
  9. Gosain AK, Rao S, Arora A (2011) Climate change impact assessment of water resources of India. Curr Sci 101:356–371Google Scholar
  10. Gu JP, Wang SY, Gong JG, Sun ZF, Gong WL (2017) Spational-temporal evolution study of drought and flood in Heilongjiang Province. Int J Hydroelectr Energy 35(02):17–20Google Scholar
  11. Han W, Liang C, Jiang B, Ma W, Zhang Y (2016) Major natural disasters in China, 1985–2014: occurrence and damages. Int J Environ Res Public Health 13(11):E1118CrossRefGoogle Scholar
  12. Hsu KC, Li ST (2010) Clustering spatial–temporal precipitation data using wavelet transform and self-organizing map neural network. Adv Water Resour 33(2):190–200CrossRefGoogle Scholar
  13. Huang J, Sun SL, Xue Y, Li JJ, Zhang JC (2014) Spatial and temporal variability of precipitation and dryness/wetness during 1961–2008 in Sichuan Province, West China. Water Resour Manag 28(6):1655–1670CrossRefGoogle Scholar
  14. Huang J, Liu FG, Xue Y, Sun SL (2015) The spatial and temporal analysis of precipitation concentration and dry spell in Qinghai, northwest China. Stoch Env Res Risk Assess 29(5):1403–1411CrossRefGoogle Scholar
  15. Jiang XY, Fan JB, Zhang JQ, Tong ZJ, Liu XP (2009) Gis-based risk assessment on rain and flood disasters of Songhua river. J Catastr 24(03):51–56Google Scholar
  16. Jiang C, Li DQ, Gao YN, Liu WF, Zhang LB (2016) Impact of climate variability and anthropogenic activity on streamflow in the Three Rivers Headwater Region, Tibetan Plateau, China. Theoret Appl Climatol 129(1):1–15Google Scholar
  17. Kalayci S, Kahya E (2006) Assessment of streamflow variability modes in Turkey: 1964–1994. J Hydrol 324(1):163–177CrossRefGoogle Scholar
  18. Khan MI, Liu D, Fu Q, Saddique Q, Faiz MA, Li TX, Qamar MU, Cui S, Cheng C (2017) Projected changes of future extreme drought events under numerous drought indices in the Heilongjiang Province of China. Water Resour Manag 31:3921–3937CrossRefGoogle Scholar
  19. Li C, Wang RH (2016) Recent changes of precipitation in Gansu, Northwest China: an index-based analysis. Theoret Appl Climatol 129(1–2):1–16Google Scholar
  20. Li TX, Fu Q, Cui S, Liu D (2017) Variation of precipitation and its impact on agricultural production in Heilongjiang Province. J Irrig Drain 36(05):103–108Google Scholar
  21. Liu Y, An ZS, Ma HZ, Liu ZY, Kutzbach JK, Shi JF et al (2006) Precipitation variation in the northeastern Tibetan Plateau recorded by the tree rings since 850 AD and its relevance to the Northern Hemisphere temperature. Sci China 49(4):408–420CrossRefGoogle Scholar
  22. Liu WB, Cai TJ, Ju CY, Fu GB, Yao YF, Cui XQ (2011) Assessing vegetation dynamics and their relationships with climatic variability in Heilongjiang province, northeast China. Environ Earth Sci 64(8):2013–2024CrossRefGoogle Scholar
  23. Liu BJ, Chen XH, Chen JF, Chen XH (2016) Impacts of different threshold definition methods on analyzing temporal-spatial features of extreme precipitation in the Pearl River Basin. Stoch Environ Res Risk Assess 1–12Google Scholar
  24. Liu Y, Ren LL, Ma MW, Yang XL, Yuan F, Jiang SH (2016b) An insight into the Palmer drought mechanism based indices: comprehensive comparison of their strengths and limitations. Stoch Env Res Risk Assess 30(1):119–136CrossRefGoogle Scholar
  25. Liu YS, Yuan XM, Guo L, Huang YH, Zhang XL (2017) Driving force analysis of the temporal and spatial distribution of flash floods in Sichuan Province. Sustainability 9(9):1527CrossRefGoogle Scholar
  26. Malik N, Bookhagen B, Mucha PJ (2016) Spatiotemporal patterns and trends of Indian monsoonal rainfall extremes. Geophys Res Lett 43(4):1710CrossRefGoogle Scholar
  27. Mckee TB, Doesken NJ, Kliest J (1993) The relation-ship of drought frequency and duration to time scales. In: Proceedings of the 8th conference on applied climatology, Boston: American Meteorological Society, pp 179–182Google Scholar
  28. Min Y (2013) Spatiotemporal evolution of the droughts and floods over China. Physics 62(13):139203Google Scholar
  29. Mitra S, Srivastava P (2017) Spatiotemporal variability of meteorological droughts in southeastern USA. Nat Hazards 86(3):1007–1038CrossRefGoogle Scholar
  30. Na XD, Zang SY, Zhang NN, Cui J (2015) Impact of land use and land cover dynamics on Zhalong wetland reserve ecosystem, Heilongjiang Province, China. Int J Environ Sci Technol 12(2):445–454CrossRefGoogle Scholar
  31. Naghettini M, Gontijo NT, Portela MM (2012) Investigation on the properties of the relationship between rare and extreme rainfall and flood volumes, under some distributional restrictions. Stoch Env Res Risk Assess 26(6):859–872CrossRefGoogle Scholar
  32. Oesting M, Stein A (2018) Spatial modeling of drought events using max-stable processes. Stoch Env Res Risk Assess 32(1):63–81CrossRefGoogle Scholar
  33. Padhee SK, Nikam BR, Dutta S, Aggarwal SP (2017) Using satellite-based soil moisture to detect and monitor spatiotemporal traces of agricultural drought over Bundelkhand region of India. Mapp Sci Remote Sens 54(2):144–166Google Scholar
  34. Pei W, Fu Q, Liu D, Li TX, Cheng K (2016) Assessing agricultural drought vulnerability in the Sanjiang Plain based on an improved projection pursuit model. Nat Hazards 82(1):683–701. CrossRefGoogle Scholar
  35. Pei W, Fu Q, Li TX (2017) Spatiotemporal analysis of the agricultural drought risk in Heilongjiang Province. Theor Appl Climatol, China. Google Scholar
  36. Rudd AC, Bell VC, Kay AL (2017) National-scale analysis of simulated hydrological droughts (1891–2015). J Hydrol 550:368–385CrossRefGoogle Scholar
  37. Santos JF, Pulidocalvo I, Portela MM (2010) Spatial and temporal variability of droughts in Portugal. Water Resour Res 46(3):742–750CrossRefGoogle Scholar
  38. Santos CAG, Neto RMB, Silva RMD (2017) Drought assessment using a TRMM-derived standardized precipitation index for the upper São Francisco River basin, Brazil. Environ Monit Assess 189(6):250CrossRefGoogle Scholar
  39. Schaller C, Göckede M, Foken T (2017) Flux calculation of short turbulent events—comparison of three methods. Atmos Meas Tech 10(3):1–20CrossRefGoogle Scholar
  40. Sharma KD (2011) Rain-fed agriculture could meet the challenges of food security in India. Curr Sci 100(11):1615–1616Google Scholar
  41. She DX, Xia J, Zhu LT, Lü JM, Chen XD, Zhang LP, Zhang X (2016) Changes of rainfall and its possible reasons in the Nansi Lake Basin, China. Stoch Env Res Risk Assess 30(4):1099–1113CrossRefGoogle Scholar
  42. Shi FM, Yang B, Pei ZJ, Li Wang, Gao YB, Zuo X, Lu FY, Liu J (2017) Change characteristics of agrometeorological disaster rates in Heilongjiang Province in recent 35 years. J Northeast Agric Univ 48(10):50–56Google Scholar
  43. Singh V, Goyal MK (2016) Spatio-temporal heterogeneity and changes in extreme precipitation over eastern Himalayan catchments India. Stoch Environ Res Risk Assess 1–20Google Scholar
  44. Sun P, Zhang Q, Wen QZ, Singh VP, Shi PJ (2017) Multisource data based integrated agricultural drought monitoring in the Huai River basin, China. J Geophys Res Atmos 122(20):10751–10772CrossRefGoogle Scholar
  45. Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Am Meteorol Soc 79(79):61–78CrossRefGoogle Scholar
  46. Wang L, Yao ZJ, Jiang LG, Wang R, Wu SS, Liu ZF (2016) Changes in climate extremes and catastrophic events in the Mongolian Plateau from 1951 to 2012. J Appl Meteorol Climatol 55(5):151117070718004CrossRefGoogle Scholar
  47. Xing ZX, Yang ZR, Fu Q, Li H, Gong XL, Wu JY (2017) Characteristics and risk assessment of agricultural meteorological disasters based on 30-years’ disaster data from Heilongjiang Province of China. Int J Agric Biol Eng 10(6):144–154CrossRefGoogle Scholar
  48. Xu XH, Lv ZQ, Zhou XY, Jiang N (2016) Drought prediction and sustainable development of the ecological environment. Environ Sci Pollut Res 24(35):26974–26982CrossRefGoogle Scholar
  49. Yu ZB, Gu HH, Wang JG, Xia J, Lu BH (2017) Effect of projected climate change on the hydrological regime of the Yangtze River Basin, China. Stoch Env Res Risk Assess 4–5:1–16Google Scholar
  50. Zhang Q, Li JF, Singh VP, Xiao MZ (2013a) Spatio-temporal relations between temperature and precipitation regimes: implications for temperature-induced changes in the hydrological cycle. Global Planet Change 111(4):57–76CrossRefGoogle Scholar
  51. Zhang Q, Xiao MZ, Singh VP, Chen XH (2013b) Copula-based risk evaluation of hydrological droughts in the East River basin, China. Stoch Env Res Risk Assess 27(6):1397–1406CrossRefGoogle Scholar
  52. Zhang Q, Xiao M, Singh VP (2015a) Uncertainty evaluation of copula analysis of hydrological droughts in the East River basin. China Glob Planet Change 129:1–9CrossRefGoogle Scholar
  53. Zhang Q, Qi TY, Singh VP, Chen YQ, Xiao MZ (2015b) Regional frequency analysis of droughts in China: a multivariate perspective. Water Resour Manage 29(6):1767–1787CrossRefGoogle Scholar
  54. Zhang Q, Gu XH, Singh VP, Kong DD, Chen XH (2015c) Spatiotemporal behavior of floods and droughts and their impacts on agriculture in China. Global Planet Change 131:63–72CrossRefGoogle Scholar
  55. Zhang Q, Sun P, Li JF, Xiao MZ, Singh VP (2015d) Assessment of drought vulnerability of the Tarim River basin, Xinjiang, China. Theoret Appl Climatol 121(1–2):337–347CrossRefGoogle Scholar
  56. Zhang Q, Sun P, Li J, Singh VP, Liu J (2015e) Spatiotemporal properties of droughts and related impacts on agriculture in Xinjiang, China. Int J Climatol 35(7):1254–1266CrossRefGoogle Scholar
  57. Zhang Q, Sun P, Singh VP, Li JF, Tu XJ (2016) Evaluation of transitional behavior of wetness/drought regimes in the Poyang Lake Basin, China. Theoret Appl Climatol 126(3–4):631–642CrossRefGoogle Scholar
  58. Zhang Q, Kong DD, Singh VP, Shi PJ (2017) Response of vegetation to different time-scales drought across China: spatiotemporal patterns, causes and implications. Global Planet Change 152:1–11CrossRefGoogle Scholar
  59. Zubieta R, Saavedra M, Silva Y, Giráldez L (2016) Spatial analysis and temporal trends of daily precipitation concentration in the Mantaro River basin: central Andes of Peru. Stoch Env Res Risk Assess 31(6):1–14Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Qiang Fu
    • 1
    • 2
  • Zhaoqiang Zhou
    • 1
    • 2
  • Tianxiao Li
    • 1
    • 2
  • Dong Liu
    • 1
    • 2
  • Renjie Hou
    • 1
    • 2
  • Song Cui
    • 1
    • 2
  • Peiru Yan
    • 1
    • 2
  1. 1.Collaborative Innovation Centre of Promote Grain Production in Heilongjiang ProvinceHarbinChina
  2. 2.Northeast Agricultural UniversityHarbinChina

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