Spatiotemporal Pattern Mining of Drought in the Last 40 Years in China Based on the SPEI and Space–Time Cube

Dehe Xu aState Key Laboratory of Geo-Information Engineering, Xi’an, China
bNorth China University of Water Resources and Electric Power, Zhengzhou, China

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Qi Zhang bNorth China University of Water Resources and Electric Power, Zhengzhou, China

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Yan Ding bNorth China University of Water Resources and Electric Power, Zhengzhou, China

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De Zhang aState Key Laboratory of Geo-Information Engineering, Xi’an, China

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Abstract

Drought is a common natural disaster that greatly affects the crop yield and water supply in China. However, the spatiotemporal characteristics of drought in China are not well understood. This paper explores the spatial and temporal distributions of droughts in China over the past 40 years using multiscale standardized precipitation evapotranspiration index (SPEI) values calculated by monthly precipitation and temperature data from 612 meteorological stations in China from 1980 to 2019 and combines the space–time cube (STC), Mann–Kendall test, emerging spatiotemporal hot-spot analysis, spatiotemporal clustering, and local outliers for the analysis. The results were as follows: 1) the drought frequency and STC show that there is a significant difference in the spatiotemporal distribution of drought in China, with the most severe drought in Northwest China, followed by the western part of Southwest China and the northern part of North China. 2) The emerging spatiotemporal hot-spot analysis of SPEI6 over the past 40 years reveals two cold spots in subregion 4, indicating that future droughts in the region will be more severe. 3) A local outlier analysis of the multiscale SPEI yields a low–low outlier in western North China, indicating relatively more severe year-round drought in this area than in other areas. The low–high outlier in central China indicates that this region was not dry in the past and that drought will become more severe in this region in the future.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Qi Zhang, 895300576@qq.com

Abstract

Drought is a common natural disaster that greatly affects the crop yield and water supply in China. However, the spatiotemporal characteristics of drought in China are not well understood. This paper explores the spatial and temporal distributions of droughts in China over the past 40 years using multiscale standardized precipitation evapotranspiration index (SPEI) values calculated by monthly precipitation and temperature data from 612 meteorological stations in China from 1980 to 2019 and combines the space–time cube (STC), Mann–Kendall test, emerging spatiotemporal hot-spot analysis, spatiotemporal clustering, and local outliers for the analysis. The results were as follows: 1) the drought frequency and STC show that there is a significant difference in the spatiotemporal distribution of drought in China, with the most severe drought in Northwest China, followed by the western part of Southwest China and the northern part of North China. 2) The emerging spatiotemporal hot-spot analysis of SPEI6 over the past 40 years reveals two cold spots in subregion 4, indicating that future droughts in the region will be more severe. 3) A local outlier analysis of the multiscale SPEI yields a low–low outlier in western North China, indicating relatively more severe year-round drought in this area than in other areas. The low–high outlier in central China indicates that this region was not dry in the past and that drought will become more severe in this region in the future.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Qi Zhang, 895300576@qq.com

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  • Alley, W. M., 1984: The Palmer drought severity index: Limitations and assumptions. J. Climate Appl. Meteor., 23, 11001109, https://doi.org/10.1175/1520-0450(1984)023<1100:TPDSIL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chelton, D. B., and C. M. Risien, 2020: A hybrid precipitation index inspired by the SPI, PDSI and MCDI. Part 1: Development of the index. J. Hydrometeor., 21, 19451976, https://doi.org/10.1175/JHM-D-19-0230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, H., and J. Sun, 2015: Changes in drought characteristics over china using the standardized precipitation evapotranspiration index. J. Climate, 28, 54305447, https://doi.org/10.1175/JCLI-D-14-00707.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, X., C. Miao, Q. Duan, C. Shen, and Y. Wu, 2021: Future climate change hotspots under different 21st century warming scenarios. Earth’s Future, 9, e2021EF002027, https://doi.org/10.1029/2021EF002027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Filho, J. A. W., W. Stuerzlinger, and L. Nedel, 2020: Evaluating an immersive space–time cube geovisualization for intuitive trajectory data exploration. IEEE Trans. Vis. Comput. Graph., 26, 514524, https://doi.org/10.1109/TVCG.2019.2934415.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • García-Palacios, P., N. Gross, J. Gaitán, and F. T. Maestre, 2018: Climate mediates the biodiversity–ecosystem stability relationship globally. Proc. Natl. Acad. Sci. USA, 115, 84008405, https://doi.org/10.1073/pnas.1800425115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gebremeskel Haile, G., and Coauthors, 2020: Long-term spatiotemporal variation of drought patterns over the Greater Horn of Africa. Sci. Total Environ., 704, 135299, https://doi.org/10.1016/j.scitotenv.2019.135299.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Getis, A., and J. K. Ord, 1992: The analysis of spatial association by use of distance statistics. Geogr. Anal., 24, 189206, https://doi.org/10.1111/j.1538-4632.1992.tb00261.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gou, J., C. Miao, Q. Duan, Q. Tang, Z. Di, W. Liao, J. Wu, and R. Zhou, 2020: Sensitivity analysis-based automatic parameter calibration of the VIC model for streamflow simulations over China. Water Resour. Res., 56, e2019WR025968, https://doi.org/10.1029/2019WR025968.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gross, N., Y. Le Bagousse-pinguet, P. Liancourt, M. Berdugo, N. J. Gotelli, and F. T. Maestre, 2017: Functional trait diversity maximizes ecosystem multifunctionality. Nat. Ecol. Evol., 1, 0132, https://doi.org/10.1038/s41559-017-0132.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hägerstrand, T., 1969: What about people in regional science? Pap. Reg. Sci. Assoc., 24, 721.

  • Hayes, M., M. Svoboda, N. Wall, and M. Widhalm, 2011: The Lincoln declaration on drought indices: Universal meteorological drought index recommended. Bull. Amer. Meteor. Soc., 92, 485488, https://doi.org/10.1175/2010BAMS3103.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, Y. F., J. T. Ang, Y. J. Tiong, M. Mirzaei, and M. Z. M. Amin, 2016: Drought forecasting using SPI and EDI under RCP-8.5 climate change scenarios for Langat River Basin, Malaysia. Procedia Eng., 154, 710717, https://doi.org/10.1016/j.proeng.2016.07.573.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Javed, T., and Coauthors, 2020: Performance and relationship of four different agricultural drought indices for drought monitoring in China’s mainland using remote sensing data. Sci. Total Environ., 759, 143530, https://doi.org/10.1016/j.scitotenv.2020.143530.

    • Search Google Scholar
    • Export Citation
  • Kraak, M. J., 2006: Visualization viewpoints: Beyond geovisualization. IEEE Comput. Graph. Appl., 26, 69, https://doi.org/10.1109/MCG.2006.74.

  • Kveladze, I., M. J. Kraak, and C. P. J. M. Van Elzakker, 2015: The space–time cube as part of a GeoVisual analytics environment to support the understanding of movement data. Int. J. Geogr. Inf. Sci., 29, 20012016, https://doi.org/10.1080/13658816.2015.1058386.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leduc, T., V. Tourre, and M. Servières, 2019: The space–time cube as an effective way of representing and analysing the streetscape along a pedestrian route in an urban environment. SHS Web Conf., 64, 03005, https://doi.org/10.1051/shsconf/20196403005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, L., D. She, H. Zheng, P. Lin, and Z.-L. Yang, 2020: Elucidating diverse drought characteristics from two meteorological drought indices (SPI and SPEI) in China. J. Hydrometeor., 21, 15131530, https://doi.org/10.1175/JHM-D-19-0290.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., B. He, X. Quan, Z. Liao, and X. Bai, 2015: Use of the standardized precipitation evapotranspiration index (SPEI) to characterize the drying trend in Southwest China from 1982–2012. Remote Sens., 7, 10 91710 937, https://doi.org/10.3390/rs70810917.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., Y. Chen, G. Fang, and Y. Li, 2017: Multivariate assessment and attribution of droughts in Central Asia. Sci. Rep., 7, 1316, https://doi.org/10.1038/s41598-017-01473-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, M., X. Xu, and A. Sun, 2015: Decreasing spatial variability in precipitation extremes in southwestern China and the local/large-scale influencing factors. J. Geophys. Res., 120, 64806488, https://doi.org/10.1002/2014JD022886.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maity, R., M. Suman, and N. K. Verma, 2016: Drought prediction using a wavelet based approach to model the temporal consequences of different types of droughts. J. Hydrol., 539, 417428, https://doi.org/10.1016/j.jhydrol.2016.05.042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. Eighth Conf. on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 179–184.

  • Miao, C., Q. Duan, Q. Sun, X. Lei, and H. Li, 2019: Non-uniform changes in different categories of precipitation intensity across China and the associated large-scale circulations. Environ. Res. Lett., 14, 025004, https://doi.org/10.1088/1748-9326/aaf306.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miao, C., H. Zheng, J. Jiao, X. Feng, Q. Duan, and E. Mpofu, 2020: The changing relationship between rainfall and surface runoff on the Loess Plateau, China. J. Geophys. Res. Atmos. 125, e2019JD032053, https://doi.org/10.1029/2019JD032053.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mo, C., D. Tan, T. Mai, C. Bei, J. Qin, W. Pang, and Z. Zhang, 2020: An analysis of spatiotemporal pattern for COIVD-19 in China based on space–time cube. J. Med. Virol., 92, 15871595, https://doi.org/10.1002/jmv.25834.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peña-Gallardo, M., S. Martín Vicente-Serrano, F. Domínguez-Castro, and S. Beguería, 2019: The impact of drought on the productivity of two rainfed crops in Spain. Nat. Hazards Earth Syst. Sci., 19, 12151234, https://doi.org/10.5194/nhess-19-1215-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poornima, S., and M. Pushpalatha, 2019: Drought prediction based on SPI and SPEI with varying timescales using LSTM recurrent neural network. Soft Comput., 23, 83998412, https://doi.org/10.1007/s00500-019-04120-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purwanto, P., S. Utaya, B. Handoyo, S. Bachri, I. S. Astuti, K. Sastro, B. Utomo, and Y. E. Aldianto, 2021: Spatiotemporal analysis of COVID-19 spread with emerging hotspot analysis and space–time cube models in East Java, Indonesia. ISPRS Int. J. Geo-Inf., 10, 133, https://doi.org/10.3390/ijgi10030133.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shiru, M. S., S. Shahid, A. Dewan, E. S. Chung, N. Alias, K. Ahmed, and Q. K. Hassan, 2020: Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios. Sci. Rep., 10, 10107, https://doi.org/10.1038/s41598-020-67146-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sirami, C., and Coauthors, 2019: Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc. Natl. Acad. Sci. USA, 116, 16 44216 447, https://doi.org/10.1073/pnas.1906419116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spinoni, J., G. Naumann, H. Carrao, P. Barbosa, and J. Vogt, 2014: World drought frequency, duration, and severity for 1951–2010. Int. J. Climatol., 34, 27922804, https://doi.org/10.1002/joc.3875.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Q., C. Miao, M. Hanel, A. G. L. Borthwick, Q. Duan, D. Ji, and H. Li, 2019: Global heat stress on health, wildfires, and agricultural crops under different levels of climate warming. Environ. Int., 128, 125136, https://doi.org/10.1016/j.envint.2019.04.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Q., C. Miao, A. A. Kouchak, I. Mallakpour, D. Ji, and Q. Duan, 2020: Possible increased frequency of ENSO-related dry and wet conditions over some major watersheds in a warming climate. Bull. Amer. Meteor. Soc., 101, E409E426, https://doi.org/10.1175/BAMS-D-18-0258.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, Y., Y. P. Xu, and G. Wang, 2018: Agricultural drought prediction using climate indices based on support vector regression in Xiangjiang River basin. Sci. Total Environ., 622–623, 710720, https://doi.org/10.1016/j.scitotenv.2017.12.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vicente-Serrano, S. M., S. Beguería, and J. I. López-Moreno, 2010: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Climate, 23, 16961718, https://doi.org/10.1175/2009JCLI2909.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, F., Z. Wang, H. Yang, and Y. Zhao, 2018: Study of the temporal and spatial patterns of drought in the Yellow River basin based on SPEI. Sci. China Earth Sci., 61, 10981111, https://doi.org/10.1007/s11430-017-9198-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., J. C. Rogers, and D. K. Munroe, 2015: Commonly used drought indices as indicators of soil moisture in China. J. Hydrometeor., 16, 13971408, https://doi.org/10.1175/JHM-D-14-0076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, W., Y. Zhu, R. Xu, and J. Liu, 2015: Drought severity change in China during 1961–2012 indicated by SPI and SPEI. Nat. Hazards, 75, 24372451, https://doi.org/10.1007/s11069-014-1436-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, D., Q. Zhang, Y. Ding, and H. Huang, 2020: Application of a hybrid ARIMA-SVR model based on the SPI for the forecast of drought—A case study in Henan Province, China. J. Appl. Meteor. Climatol., 59, 12391259, https://doi.org/10.1175/JAMC-D-19-0270.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, P., J. Xia, Y. Zhang, C. Zhan, and Y. Qiao, 2018: Comprehensive assessment of drought risk in the arid region of Northwest China based on the global palmer drought severity index gridded data. Sci. Total Environ., 627, 951962, https://doi.org/10.1016/j.scitotenv.2018.01.234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yao, N., Y. Li, T. Lei, and L. Peng, 2018: Drought evolution, severity and trends in mainland China over 1961–2013. Sci. Total Environ., 616–617, 7389, https://doi.org/10.1016/j.scitotenv.2017.10.327.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, M., Q. Li, M. J. Hayes, M. D. Svoboda, and R. R. Heim, 2014: Are droughts becoming more frequent or severe in China based on the standardized precipitation evapotranspiration index: 1951–2010? Int. J. Climatol., 34, 545558, https://doi.org/10.1002/joc.3701.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhai, J., B. Su, V. Krysanova, T. Vetter, C. Gao, and T. Jiang, 2010: Spatial variation and trends in PDSI and SPI indices and their relation to streamflow in 10 large regions of China. J. Climate, 23, 649663, https://doi.org/10.1175/2009JCLI2968.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, B., C. He, M. Burnham, and L. Zhang, 2016: Evaluating the coupling effects of climate aridity and vegetation restoration on soil erosion over the Loess Plateau in China. Sci. Total Environ., 539, 436449, https://doi.org/10.1016/j.scitotenv.2015.08.132.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., F. Sun, J. Xu, Y. Chen, Y. F. Sang, and C. Liu, 2016: Dependence of trends in and sensitivity of drought over China (1961–2013) on potential evaporation model. Geophys. Res. Lett., 43, 206213, https://doi.org/10.1002/2015GL067473.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Q., H. Yu, P. Sun, V. P. Singh, and P. Shi, 2019: Multisource data based agricultural drought monitoring and agricultural loss in China. Global Planet. Change, 172, 298306, https://doi.org/10.1016/j.gloplacha.2018.10.017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., G. Gao, W. An, X. Zou, H. Li, and M. Hou, 2017: Timescale differences between SC-PDSI and SPEI for drought monitoring in China. Phys. Chem. Earth, 102, 4858, https://doi.org/10.1016/j.pce.2015.10.022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M., A. Geruo, I. Velicogna, and J. S. Kimball, 2017: A global gridded dataset of GRACE drought severity index for 2002–14: Comparison with PDSI and SPEI and a case study of the Australia millennium drought. J. Hydrometeor., 18, 21172129, https://doi.org/10.1175/JHM-D-16-0182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, S., D. Cong, K. He, H. Yang, and Z. Qin, 2017: Spatial-temporal variation of drought in China from 1982 to 2010 based on a modified temperature vegetation drought index (mTVDI). Sci. Rep., 7, 17473, https://doi.org/10.1038/s41598-017-17810-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, Y., L. Ge, J. Liu, H. Liu, L. Yu, N. Wang, Y. Zhou, and X. Ding, 2019: Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: A space–time cube-based approach. J. Int. Med. Res., 47, 33713388, https://doi.org/10.1177/0300060519850734.

    • Crossref
    • Search Google Scholar
    • Export Citation
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