Abstract
Extreme precipitation events bring considerable risks to the natural ecosystem and human life. Investigating the spatial-temporal characteristics of extreme precipitation and predicting it quantitatively are critical for the flood prevention and water resources planning and management. In this study, daily precipitation data (1957–2019) were collected from 24 meteorological stations in the Weihe River Basin (WRB), Northwest China and its surrounding areas. We first analyzed the spatial-temporal change of precipitation extremes in the WRB based on space- time cube (STC), and then predicted precipitation extremes using long short-term memory (LSTM) network, auto-regressive integrated moving average (ARIMA), and hybrid ensemble empirical mode decomposition (EEMD)-LSTM-ARIMA models. The precipitation extremes increased as the spatial variation from northwest to southeast of the WRB. There were two clusters for each extreme precipitation index, which were distributed in the northwestern and southeastern or northern and southern of the WRB. The precipitation extremes in the WRB present a strong clustering pattern. Spatially, the pattern of only high-high cluster and only low-low cluster were primarily located in lower reaches and upper reaches of the WRB, respectively. Hot spots (25.00%–50.00%) were more than cold spots (4.17%–25.00%) in the WRB. Cold spots were mainly concentrated in the northwestern part, while hot spots were mostly located in the eastern and southern parts. For different extreme precipitation indices, the performances of the different models were different. The accuracy ranking was EEMD-LSTM-ARIMA > LSTM > ARIMA in predicting simple daily intensity index (SDII) and consecutive wet days (CWD), while the accuracy ranking was LSTM > EEMD-LSTM-ARIMA > ARIMA in predicting very wet days (R95P). The hybrid EEMD-LSTM-ARIMA model proposed was generally superior to single models in the prediction of precipitation extremes.
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References
Alexander L V, Zhang X, Peterson T C et al., 2006. Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research:Atmospheres, 111(D5): D05109. doi: https://doi.org/10.1029/2005JD006290
Allan R P, Soden B J, 2008. Atmospheric warming and the amplification of precipitation extremes. Science, 321(5895): 1481–1484. doi: https://doi.org/10.1126/science.1160787
Chang J X, Wang Y M, Istanbulluoglu E et al., 2015. Impact of climate change and human activities on runoff in the Weihe River Basin, China. Quaternary International, 380–381: 169–179. doi: https://doi.org/10.1016/j.quaint.2014.03.048
Charrad M, Ghazzali N, Boiteau V et al., 2014. NbClust: an R package for determining the relevant number of clusters in a data set. Journal of Statistical Software, 61(6): 1–36. doi: https://doi.org/10.18637/jss.v061.i06
Choi G, Collins D, Ren G Y et al., 2010. Changes in means and extreme events of temperature and precipitation in the Asia-Pacific Network region, 1955–2007. International Journal of Climatology, 29(13): 1906–1925. doi: https://doi.org/10.1002/joc.1979
Croitoru A E, Piticar A, Burada D C, 2016. Changes in precipitation extremes in Romania. Quaternary International, 415: 325–335. doi: https://doi.org/10.1016/j.quaint.2015.07.028
Diffenbaugh N S, Singh D, Mankin J S et al., 2017. Quantifying the influence of global warming on unprecedented extreme climate events. Proceedings of the National Academy of Sciences of the United States of America, 114(19): 4881–4886. doi: https://doi.org/10.1073/pnas.1618082114
Donat M G, Lowry A L, Alexander L V et al., 2016. More extreme precipitation in the world’s dry and wet regions. Nature Climate Change, 6(5): 508–513. doi: https://doi.org/10.1038/nclimate2941
dos Santos C A C, Neale C M U, Rao T V R et al., 2011. Trends in indices for extremes in daily temperature and precipitation over Utah, USA. International Journal of Climatology, 31(12): 1813–1822. doi: https://doi.org/10.1002/joc.2205
Du M C, Zhang J Y, Yang Q L et al., 2021. Spatial and temporal variation of rainfall extremes for the North Anhui Province Plain of China over 1976–2018. Natural Hazards, 105(3): 2777–2797. doi: https://doi.org/10.1007/s11069-020-04423-9
Durdu Ö F, 2010. Application of linear stochastic models for drought forecasting in the Büyük Menderes River Basin, western Turkey. Stochastic Environmental Research and Risk Assessment, 24(8): 1145–1162. doi: https://doi.org/10.1007/s00477-010-0366-3
Fan X H, Wang Q X, Wang M B, 2012. Changes in temperature and precipitation extremes during 1959–2008 in Shanxi, China. Theoretical and Applied Climatology, 109(1–2): 283–303. doi: https://doi.org/10.1007/s00704-011-0577-7
Farajzadeh J, Fard A F, Lotfi S, 2014. Modeling of monthly rainfall and runoff of Urmia lake basin using ‘feed-forward neural network’ and ‘time series analysis’ model. Water Resources and Industry, 7–8: 38–48. doi: https://doi.org/10.1016/j.wri.2014.10.003
Gatalsky P, Andrienko N, Andrienko G, 2004. Interactive analysis of event data using space-time cube. In: Proceedings of the 8th International Conference on Information Visualisation. London, UK: IEEE, 145–152. doi: https://doi.org/10.1109/iv.2004.1320137
George J, Janaki L, Gomathy J P, 2016. Statistical downscaling using local polynomial regression for rainfall predictions—a case study. Water Resources Management, 30(1): 183–193. doi: https://doi.org/10.1007/s11269-015-1154-0
Ghosh S, Das D, Kao S C et al., 2012. Lack of uniform trends but increasing spatial variability in observed Indian rainfall extremes. Nature Climate Change, 2(2): 86–91. doi: https://doi.org/10.1038/nclimate1327
Greff K, Srivastava R K, Koutník J et al., 2017. LSTM: A search space Odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28: 2222–2232. doi: https://doi.org/10.1109/TNNLS.2016.2582924
Groisman P Y, Knight R W, Easterling D R et al., 2005. Trends in intense precipitation in the climate record. Journal of Climate, 18(9): 1326–1350. doi: https://doi.org/10.1175/JCLI3339.1
Gu X H, Zhang Q, Li J F et al., 2019. Impact of urbanization on nonstationarity of annual and seasonal precipitation extremes in China. Journal of Hydrology, 575: 638–655. doi: https://doi.org/10.1016/j.jhydrol.2019.05.070
Hochreiter S, Schmidhuber J, 1997. Long short-term memory. Neural Computation, 9(8): 1735–1780. doi: https://doi.org/10.1162/neco.1997.9.8.1735
Huang N E, Wu M L C, Long S R et al., 2003. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proceedings of the Royal Society A:Mathematical, Physical and Engineering Sciences, 459(2037): 2317–2345. doi: https://doi.org/10.1098/rspa.2003.1123
The Intergovernmental Panel on Climate Change (IPCC), 2013. Summary for policymakers. In: Climate Change 2013: the Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 2013. Cambridge: Cambridge University Press.
Karl T R, Knight R W, Easterling D R et al., 1996. Indices of climate change for the United States. Bulletin of the American Meteorological Society, 77(2): 279–292. doi: https://doi.org/10.1175/1520-0477(1996)077<0279:IOCCFT>2.0.CO;2
Kaufmann R K, Seto K C, Schneider A et al., 2007. Climate response to rapid urban growth: evidence of a human-induced precipitation deficit. Journal of Climate, 20: 2299–2306. doi: https://doi.org/10.1175/JCLI4109.1
Kelly-Voicu E, 2019. Exploratory data analysis of the California wildfires space-time pattern. Available at https://www.researchgate.net/publication/337810548_Exploratory_Data_Analysis_of_the_California_Wildfires_Space-Time_Pattern
Kishtawal C., Niyogi D, Tewari M et al., 2010. Urbanization signature in the observed heavy rainfall climatology over India. International Journal of Climatology, 30: 1908–1916. doi: https://doi.org/10.1002/joc.2044
Knapp A K, Beier C, Briske D D et al., 2008. Consequences of more extreme precipitation regimes for terrestrial ecosystems. BioScience, 58(9): 811–821. doi: https://doi.org/10.1641/B580908
Kusangaya S, Warburton M L, van Garderen E A et al., 2014. Impacts of climate change on water resources in southern Africa: a review. Physics and Chemistry of the Earth, Parts A/B/C, 67–69: 47–54. doi: https://doi.org/10.1016/j.pce.2013.09.014
Lempel A, Ziv J, 1976. On the complexity of finite sequences. IEEE Transactions on Information Theory, 22(1): 75–81. doi: https://doi.org/10.1109/TIT.1976.1055501
Li W, Kiaghadi A, Dawson C, 2021a. Exploring the best sequence LSTM modeling architecture for flood prediction. Neural Computing and Applications, 33(11): 5571–5580. doi: https://doi.org/10.1007/s00521-020-05334-3
Li X, Wang X, Babovic V, 2018. Analysis of variability and trends of precipitation extremes in Singapore during 1980–2013. International Journal of Climatology, 38(1): 125–141. doi: https://doi.org/10.1002/joc.5165
Li X, Zhang K, Gu P R et al., 2021b. Changes in precipitation extremes in the Yangtze River Basin during 1960–2019 and the association with global warming, ENSO, and local effects. Science of the Total Environment, 760: 144244. doi: https://doi.org/10.1016/j.scitotenv.2020.144244
Liang L Y, Cai X Y, 2020. Forecasting peer-to-peer platform default rate with LSTM neural network. Electronic Commerce Research and Applications, 43: 100997. doi: https://doi.org/10.1016/j.elerap.2020.100997
Limsakul A, Singhruck P, 2016. Long-term trends and variability of total and extreme precipitation in Thailand. Atmospheric Research, 169: 301–317. doi: https://doi.org/10.1016/j.atmosres.2015.10.015
Liu Q J, Zhang H Y, Gao K T et al., 2019. Time-frequency analysis and simulation of the watershed suspended sediment concentration based on the Hilbert-Huang transform (HHT) and artificial neural network (ANN) methods: a case study in the Loess Plateau of China. Catena, 179: 107–118. doi: https://doi.org/10.1016/j.catena.2019.03.042
Liu S Y, Huang S Z, Huang Q et al., 2017. Identification of the non-stationarity of extreme precipitation events and correlations with large-scale ocean-atmospheric circulation patterns: a case study in the Wei River Basin, China. Journal of Hydrology, 548: 184–195. doi: https://doi.org/10.1016/j.jhydrol.2017.03.012
Maheswaran R, Khosa R, 2014. A wavelet-based second order nonlinear model for forecasting monthly rainfall. Water Resources Management, 28(15): 5411–5431. doi: https://doi.org/10.1007/s11269-014-0809-6
Meng E H, Huang S Z, Huang Q, 2019. A robust method for nonstationary streamflow prediction based on improved EMDSVM model. Journal of Hydrology, 568: 462–478. doi: https://doi.org/10.1016/j.jhydrol.2018.11.015
Moanga D, Biging G, Radke J et al., 2021. The space-time cube as an approach to quantifying future wildfires in California. International Journal of Wildland Fire, 30(2): 139–153. doi: https://doi.org/10.1071/WF19062
Mondal A, Mujumdar P P, 2015. Modeling non-stationarity in intensity, duration and frequency of extreme rainfall over India. Journal of Hydrology, 521: 217–231. doi: https://doi.org/10.1016/j.jhydrol.2014.11.071
Nandintsetseg B, Greene J S, Goulden C E, 2007. Trends in extreme daily precipitation and temperature near lake Hövsgöl, Mongolia. International Journal of Climatology, 27(3): 341–347. doi: https://doi.org/10.1002/joc.1404
Ni L L, Wang D, Singh V P et al., 2020. Streamflow and rainfall forecasting by two long short-term memory-based models. Journal of Hydrology, 583: 124296. doi: https://doi.org/10.1016/j.jhydrol.2019.124296
Ouyang Q, Lu W X, Xin X et al., 2016. Monthly rainfall forecasting using EEMD-SVR based on phase-space reconstruction. Water Resources Management, 30(7): 2311–2325. doi: https://doi.org/10.1007/s11269-016-1288-8
Qin Y H, Li B F, Chen Z S et al., 2018. Spatio-temporal variations of nonlinear trends of precipitation over an arid region of northwest China according to the extreme-point symmetric mode decomposition method. International Journal of Climatology, 38(5): 2239–2249. doi: https://doi.org/10.1002/joc.5330
Ralph F M, Dettinger M, White A et al., 2014. A vision for future observations for western U. S. extreme precipitation and flooding. Journal of Contemporary Water Research & Education, 153(1): 16–32. doi: https://doi.org/10.1111/j.1936-704X.2014.03176.x
Raper J, Livingstone D, 1995. Development of a geomorphological spatial model using object-oriented design. International Journal of Geographical Information Systems, 9(4): 359–383. doi: https://doi.org/10.1080/02693799508902044
Risser M D, Wehner M F, 2017. Attributable human-induced changes in the likelihood and magnitude of the observed extreme precipitation during Hurricane Harvey. Geophysical Research Letters, 44(24): 12457–12464. doi: https://doi.org/10.1002/2017GL075888
Shao Y T, Mu X M, He Y et al., 2019. Spatiotemporal variations of extreme precipitation events at multi-time scales in the Qinling-Daba mountains region, China. Quaternary International, 525: 89–102. doi: https://doi.org/10.1016/j.quaint.2019.07.029
Sheikh M M, Manzoor N, Ashraf J et al., 2015. Trends in extreme daily rainfall and temperature indices over South Asia. International Journal of Climatology, 35(7): 1625–1637. doi: https://doi.org/10.1002/joc.4081
Siami-Namini S, Tavakoli N, Namin A S, 2018. A comparison of ARIMA and LSTM in forecasting time series. In: Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Orlando: IEEE, 1394–1401. doi: https://doi.org/10.1109/ICMLA.2018.00227
Song X, Zhang Z, Chen Y et al., 2014. Spatiotemporal changes of global extreme temperature events (ETEs) since 1981 and the meteorological causes. Natural Hazards, 70(2): 975–994. doi: https://doi.org/10.1007/s11069-013-0856-y
Tan Q F, Lei X H, Wang X et al., 2018. An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach. Journal of Hydrology, 567: 767–780. doi: https://doi.org/10.1016/j.jhydrol.2018.01.015
Tian Y, Xu Y P, Yang Z L et al., 2018. Integration of a parsimonious hydrological model with recurrent neural networks for improved streamflow forecasting. Water, 10(11): 1655. doi: https://doi.org/10.3390/w10111655
van den Besselaar E J M, Tank A M G K, Buishand T A, 2013. Trends in European precipitation extremes over 1951–2010. International Journal of Climatology, 32(12): 2682–2689. doi: https://doi.org/10.1002/joc.3619
Wang Liwei, Zhang Mingjun, Wang Shengjie et al., 2020. Stable isotope signatures and moisture transport of a typical heavy precipitation case in the Southern Tianshan Mountains. Chinese Geographical Science, 30(1): 180–188. doi: https://doi.org/10.1007/s11769-019-1091-6
Wang L Y, Chen S F, Zhu W B et al., 2021. Spatiotemporal variations of extreme precipitation and its potential driving factors in China’s North-South Transition Zone during 1960–2017. Atmospheric Research, 252: 105429. doi: https://doi.org/10.1016/j.atmosres.2020.105429
Wang T, Zhang M C, Yu Q H et al., 2012. Comparing the applications of EMD and EEMD on time-frequency analysis of seismic signal. Journal of Applied Geophysics, 83: 29–34. doi: https://doi.org/10.1016/j.jappgeo.2012.05.002
Wang W C, Xu D M, Chau K W et al., 2013. Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD. Journal of Hydroinformatics, 15(4): 1377–1390. doi: https://doi.org/10.2166/hydro.2013.134
Wu Z H, Huang N E, 2009. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(1): 1–41. doi: https://doi.org/10.1142/S1793536909000047
Xia Jun, She Dunxian, Zhang Yongyong et al., 2012. Spatio-temporal trend and statistical distribution of extreme precipitation events in Huaihe River Basin during 1960–2009. Journal of Geographical Sciences, 22(2): 195–208. doi: https://doi.org/10.1007/s11442-012-0921-6
Xue T T, Tang G P, Sun L et al., 2017. Long-term trends in precipitation and precipitation extremes and underlying mechanisms in the U. S. Great Basin during 1951–2013. Journal of Geophysical Research:Atmospheres, 122(12): 6152–6169. doi: https://doi.org/10.1002/2017JD026682
Zhang X, Feng Y, 2004. RClimDex (1. 0) User Manual. Ontario: Climate Research Branch Environment Canada Downs View.
Zhang D L, 2020. Rapid urbanization and more extreme rainfall events. Science Bulletin, 65: 516–518. doi: https://doi.org/10.1016/j.scib.2020.02.002
Zhao M, Pitman A J, 2002. The impact of land cover change and increasing carbon dioxide on the extreme and frequency of maximum temperature and convective precipitation. Geophysical Research Letters, 29(6): 2-1–2-4. doi: https://doi.org/10.1029/2001GL013476
Zhao X H, Chen X, 2015. Auto regressive and ensemble empirical mode decomposition hybrid model for annual runoff forecasting. Water Resources Management, 29(8): 2913–2926. doi: https://doi.org/10.1007/s11269-015-0977-z
Zheng J, Xu C C, Zhang Z A et al., 2017. Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network. In: Proceedings of the 51st Annual Conference on Information Sciences and Systems. Baltimore: IEEE, 1–6. doi: https://doi.org/10.1109/CISS.2017.7926112
Zhou Qi, Zhang Haining, Ren Yuanxin, 2020. Extreme precipitation events in the Weihe River Basin from 1961 to 2016. Scientia Geographica Sinica, 40(5): 833–841. (in Chinese)
Zou Lei, Yu Jiangyou, Wang Feiyu et al., 2021. Spatial-temporal variations of extreme precipitation indices and their response to atmospheric circulation factors in the Weihe River Basin. Arid Zone Research, 38(3): 764–774. (in Chinese)
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Foundation item: Under the auspices of National Key Research and Development Program of China (No. 2017YFE0118100-1)
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Qiu, D., Wu, C., Mu, X. et al. Spatial-temporal Analysis and Prediction of Precipitation Extremes: A Case Study in the Weihe River Basin, China. Chin. Geogr. Sci. 32, 358–372 (2022). https://doi.org/10.1007/s11769-022-1271-7
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DOI: https://doi.org/10.1007/s11769-022-1271-7