Analysis of the spatiotemporal patterns and propagation characteristics of drought risk in China

Dandan WANG, Huicong JIA, Jia TANG, Nanjiang LIU

Front. Earth Sci. ››

PDF(3886 KB)
Front. Earth Sci. All Journals
PDF(3886 KB)
Front. Earth Sci. ›› DOI: 10.1007/s11707-024-1139-5
RESEARCH ARTICLE

Analysis of the spatiotemporal patterns and propagation characteristics of drought risk in China

Author information +
History +

Abstract

Based on standardized precipitation index data, a systematic analysis was conducted of the spatiotemporal variations of drought events in China from 1978 to 2018. Drought events were identified using the run theory applied to the standardized precipitation index data set, and key variables such as drought frequency, duration, and intensity were quantified. Additionally, drought vulnerability, exposure, and resilience were calculated to comprehensively assess the regional drought risk. The spatiotemporal transmission characteristics and pathways of drought risk were further explored using the Markov chain model and its extended version based on spatial lag theory. The results revealed significant differences in the spatial and temporal distribution of drought events across China, with north-west China experiencing a particularly high frequency, duration, and intensity of droughts. Overall, the pattern of drought risk presented a gradient, being higher in the north-west and lower in the south-east. The risk was relatively stable from year to year, with few large fluctuations. Moreover, a strong spatial similarity in drought risk was observed among neighboring provinces, but there was no obvious spatial lag effect. This study provides a valuable scientific foundation for effective drought disaster risk management and the formulation of response measures.

Graphical abstract

Keywords

standardized precipitation index / drought risk / Markov chain / risk propagation

Cite this article

Download citation ▾
Dandan WANG, Huicong JIA, Jia TANG, Nanjiang LIU. Analysis of the spatiotemporal patterns and propagation characteristics of drought risk in China. Front. Earth Sci., https://doi.org/10.1007/s11707-024-1139-5
This is a preview of subscription content, contact us for subscripton.

References

[1]
Alam N M, Sharma G C, Moreira E, Jana C, Mishra P K, Sharma N K, Mandal D (2017). Evaluation of drought using SPEI drought class transitions and log-linear models for different agro-ecological regions of India.Phys Chem Earth, 100: 31–43
CrossRef Google scholar
[2]
Anderson T W, Goodman L A (1957). Statistical inference about Markov chains.Ann Math Stat, 28(1): 89–110
CrossRef Google scholar
[3]
Chai Y F, Li Y T, Yang Y P, Li S X, Zhang W, Ren J Q, Xiong H B (2019). Water level variation characteristics under the impacts of extreme drought and the operation of the Three Gorges Dam.Front Earth Sci, 13(3): 510–522
CrossRef Google scholar
[4]
Chiang F, Mazdiyasni O, AghaKouchak A (2021). Evidence of anthropogenic impacts on global drought frequency, duration, and intensity.Nat Commun, 12(1): 2754
CrossRef Google scholar
[5]
Dai A G (2013). Increasing drought under global warming in observations and models.Nat Clim Chang, 3(1): 52–58
CrossRef Google scholar
[6]
Fadhil R M, Unami K (2021). A multi-state Markov chain model to assess drought risks in rainfed agriculture: a case study in the Nineveh Plains of Northern Iraq.Stochastic Environ Res Risk Assess, 35(9): 1931–1951
CrossRef Google scholar
[7]
Gu L, Chen J, Yin J B, Guo Q, Wang H M, Zhou J Z (2021). Risk propagation from meteorological to hydrological droughts in a changing climate for main catchments in China.Adv Water Sci, 32(3): 321–333
[8]
Guo Y, Huang S Z, Huang Q, Wang H, Wang L, Fang W (2019). Copulas-based bivariate socioeconomic drought dynamic risk assessment in a changing environment.J Hydrol (Amst), 575: 1052–1064
CrossRef Google scholar
[9]
Hayes M J, Svoboda M D, Wilhite D A, Vanyarkho O V (1999). Monitoring the 1996 drought using the standardized precipitation index.Bull Am Meteorol Soc, 80(3): 429–438
CrossRef Google scholar
[10]
Hu S L, Jiao S T, Zhang X Q (2021). Spatio-temporal evolution and influencing factors of China’s tourism development: based on the non-static spatial Markov chain model.J Nat Resourc, 36(4): 854–865
CrossRef Google scholar
[11]
Jia H C, Chen F, Du E Y, Wang L (2023). Drought vulnerability curves based on remote sensing and historical disaster dataset.Remote Sens (Basel), 15(3): 858
CrossRef Google scholar
[12]
Jia H C, Chen F, Zhang C R, Dong J W, Du E Y, Wang L (2022). High emissions could increase the future risk of maize drought in China by 60–70%.Sci Total Environ, 852: 158474
CrossRef Google scholar
[13]
Jia Y T, Cui X Y, Liu Y X, Liu Y L, Xu C, Li T, Ran Q W, Wang Y F (2020). Drought vulnerability assessment in inner Mongolia.Acta Ecol Sin, 40(24): 9070–9082
[14]
Kang L, Wen Y L, Zhou L W, Chen H, Ye J W (2023). Drought driving mechanism and risk situation prediction based on machine learning models in the Yellow River Basin, China.Geomatics Nat Hazards Risk, 14(1): 2279493
CrossRef Google scholar
[15]
McKeeT B, Doesken N J, KleistJ (1993). The relationship of drought frequency and duration to time scales. In: Proceedings of Vulnerability. Cambridge: Cambridge University Press
[16]
Merabti A, Martins D S, Meddi M, Pereira L S (2018). Spatial and time variability of drought based on SPI and RDI with various time scales.Water Resour Manage, 32(3): 1087–1100
CrossRef Google scholar
[17]
MishraK A, SinghP V (2010). A review of drought concepts. J Hydrol (Amst), 391(1−2): 204–216
[18]
Mondal S, Mishra A K, Leung R, Cook B (2023). Global droughts connected by linkages between drought hubs.Nat Commun, 14(1): 144
CrossRef Google scholar
[19]
Nie M Q, Huang S Z, Leng G Y, Zhou Y L, Huang Q, Dai M (2021). Bayesian-based time-varying multivariate drought risk and its dynamics in a changing environment.Catena, 204: 105429
CrossRef Google scholar
[20]
Nie MQ, Huang SZ, Huang Q, Wang L, Zhang Y, Guo Y (2020). Meteorological-hydrological drought risk assessment and dynamic evolution based on nonparametric method.J Nat Disasters, 29(2): 149–160
[21]
Otkin J A, Anderson M C, Hain C, Svoboda M, Johnson D, Mueller R, Tadesse T, Wardlow B, Brown J (2016). Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought.Agric For Meteorol, 218-219: 230–242
CrossRef Google scholar
[22]
Paulo A, Pereira L S (2007). Prediction of SPI drought class transitions using Markov chains.Water Resour Manage, 21(10): 1813–1827
CrossRef Google scholar
[23]
Pu Y X, Ma R H, Ge Y, Huang X Y (2005). Spatial-temporal dynamics of Jiangsu regional convergence with spatial Markov chains approach.Acta Geograph Sin, 60(5): 817–826
[24]
Qian Z A, Wu T W, Song M H, Ma X B, Cai Y, Liang X Y (2001). Arid disaster and advances in arid climate researches over Northwest China.Adv Earth Sci, 16(1): 28–38
[25]
Sherwood S, Fu Q (2014). A drier future.Science, 343(6172): 737–739
CrossRef Google scholar
[26]
Shi Y Z, Wang J, Wang Z Q, Lu D M, Yang X J (2017). Rural household vulnerability to drought and adaptation mechanism on the Loess Plateau.Prog Geogr, 36(10): 1281–1293
CrossRef Google scholar
[27]
Stagge J H, Tallaksen L M, Gudmundsson L, Van Loon A F, Stahl K (2015). Candidate distributions for climatological drought indices (SPI and SPEI).Int J Climatol, 35(13): 4027–4040
CrossRef Google scholar
[28]
Sun P, Zhang Q, Bai Y G, Zhang J H, Deng X Y, Liu J Y (2014). Transitional behaviors of hydrometeorological droughts in Xinjiang using the Markov chain model.Geogr Res, 33(9): 1647–1657
[29]
Sun Y, Fu R, Dickinson R, Joiner J, Frankenberg C, Gu L H, Xia Y L, Fernando N (2015). Drought onset mechanisms revealed by satellite solar-induced chlorophyll fluorescence: insights from two contrasting extreme events.J Geophys Res Biogeosci, 120(11): 2427–2440
CrossRef Google scholar
[30]
Veettil A V, Konapala G, Mishra A K, Li H Y (2018). Sensitivity of drought resilience vulnerability- exposure to hydrologic ratios in contiguous United States.J Hydrol (Amst), 564: 294–306
CrossRef Google scholar
[31]
Wang F, Lai H X, Men R Y, Wang Z P, Li Y B, Qu Y P, Feng K, Guo WX, Jiang Y Z (2024). Dynamic variations of agricultural drought and its response to meteorological drought: a drought event-based perspective.J Geophys Res: Atmosph, 129(12): e2024JD041044
CrossRef Google scholar
[32]
Wang J S, Li Y H, Wang R N, Feng J Y, Zhao Y X (2012). Preliminary analysis on the demand and review of progress in the field of meteorological drought research.J Arid Meteor, 30(4): 497–508
[33]
Wang S J, Wang Y, Zhao Y B (2015). Spatial spillover effects and multi-mechanism for regional development in Guangdong province since 1990s.Acta Geogr Sin, 70(6): 965–979
[34]
Xiang Y Y, Wang Y, Chen Y N, Zhang Q F, Li H W (2022). Comprehensive evaluation of hydrological drought characteristics and their relationship to meteorological droughts in the upper Tarim River Basin, central Asia.Front Earth Sci, 16(4): 890–905
CrossRef Google scholar
[35]
Xiao M Z, Zhang Q, Chen X H (2012). Spatial-temporal Patterns of Drought Risk across the Pearl River Basin.Acta Geograph Sin, 67(1): 83–92
[36]
Xu K, Yang D W, Xu X Y, Lei H M (2015). Copula based drought frequency analysis considering the spatio-temporal variability in Southwest China.J Hydrol (Amst), 527: 630–640
CrossRef Google scholar
[37]
Xu X L, Peng T, Lin Q X, Dong X H, Wang Y X, Liu J, Chang W J, Wang G X (2022). Analysis on characteristics and driving factors of drought risk transmission in southwest China.J Hydroelect Eng, 41(12): 69–79
[38]
Yang Q, Li M X, Zheng Z Y, Ma Z G (2017). Regional applicability of seven meteorological drought indices in China.Sci China Earth Sci, 60(4): 745–760
CrossRef Google scholar
[39]
Yang W T, Deng M, Tang J B, Jin R (2020). On the use of Markov chain models for drought class transition analysis while considering spatial effects.Nat Hazards, 103(3): 2945–2959
CrossRef Google scholar
[40]
Yoo J Y, Kim U, Kim T W (2013). Bivariate drought frequency curves and confidence intervals: a case study using monthly rainfall generation.Stochastic Environ Res Risk Assess, 27(1): 285–295
CrossRef Google scholar
[41]
Zhai P M, Zou X K (2005). Changes in temperature and precipitation and their impacts on drought in China during 1951−2003.Adv Clim Chang Res, 1(1): 16–18
[42]
Zhang Q, Yao Y B, Li Y H, Huang J P, Ma Z G, Wang Z L, Wang S P, Wang Y, Zhang Y (2020). Progress and prospect on the study of causes and variation regularity of droughts in China.Acta Meteorol Sin, 78(3): 500–521
[43]
Zhang Q, Yao Y B, Wang Y, Wang S P, Wang J S, Yang J H, Wang J, Li Y P, Shang J L, Li W J (2019). Characteristics of drought in Southern China under climatic warming, the risk, and countermeasures for prevention and control.Theor Appl Climatol, 136(3−4): 1157–1173
CrossRef Google scholar
[44]
Zhang Y, Hu X, Zhang Z X, Kong R, Peng Z H, Zhang Q, Chen X (2023). The increasing risk of future simultaneous droughts over the Yangtze River basin based on CMIP6 models.Stochastic Environ Res Risk Assess, 37(7): 2577–2601
CrossRef Google scholar
[45]
Zuo Q, Jiang L, Feng Y, Diao Y (2020). Spatiotemporal variation of ecological footprint of water resources in the provinces in the Yellow River basin.J Irrigatd Drain, 39(10): 1–8

Acknowledgments

This work was supported by National Key R&D Program of China (No. 2022YFC3002805), and the National Natural Science Foundation of China (Grant No. 42171078). The authors also would like to appreciate the editors and the three anonymous reviewers for their constructive comments and advice. The authors declare no conflict of interest.

Competing interests

The authors declare that they have no competinginterests.

RIGHTS & PERMISSIONS

2024 Higher Education Press
AI Summary AI Mindmap
PDF(3886 KB)

22

Accesses

0

Citations

Detail

Sections
Recommended

/