Spatial Heterogeneity Analysis of Short-Duration Extreme Rainfall Events in Megacities in China
Tóm tắt
Given the fact that researchers require more specific spatial rainfall information for storm flood calculation, hydrological risk assessment, and water budget estimates, there is a growing need to analyze the spatial heterogeneity of rainfall accurately. This paper provides insight into rainfall spatial heterogeneity in urban areas based on statistical analysis methods. An ensemble of short-duration (3-h) extreme rainfall events for four megacities in China are extracted from a high-resolution gridded rainfall dataset (resolution of 30 min in time, 0.1° × 0.1° in space). Under the heterogeneity framework using Moran’s I, LISA (Local Indicators of Spatial Association), and semi-variance, the multi-scale spatial variability of extreme rainfall is identified and assessed in Shanghai (SH), Beijing (BJ), Guangzhou (GZ), and Shenzhen (SZ). The results show that there is a pronounced spatial heterogeneity of short-duration extreme rainfall in the four cities. Heterogeneous characteristics of rainfall within location, range, and directions are closely linked to the different urban growth in four cities. The results also suggest that the spatial distribution of rainfall cannot be neglected in the design storm in urban areas. This paper constitutes a useful contribution to quantifying the degree of spatial heterogeneity and supports an improved understanding of rainfall/flood frequency analysis in megacities.
Từ khóa
Tài liệu tham khảo
Westra, 2014, Future changes to the intensity and frequency of short-duration extreme rainfall, Rev. Geophys., 52, 525, 10.1002/2014RG000464
Wright, 2013, Estimating the frequency of extreme rainfall using weather radar and stochastic storm transposition, J. Hydrol., 488, 155, 10.1016/j.jhydrol.2013.03.003
Adnan, 2020, The potential of tidal river management for flood alleviation in south western Bangladesh, Sci. Total Environ., 731, 138747, 10.1016/j.scitotenv.2020.138747
World Meteorological Organization (2020). WMO Statement on the State of the Global Climate in 2019, World Meteorological Organization.
Abd-Elhamid, H.F., Zeleňáková, M., Vranayová, Z., and Fathy, I. (2020). Evaluating the impact of urban growth on the design of storm water drainage systems. Water, 12.
Szwagrzyk, 2018, Impact of forecasted land use changes on flood risk in the Polish Carpathians, Nat. Hazards, 94, 227, 10.1007/s11069-018-3384-y
Wang, 2020, Double increase in precipitation extremes across China in a 1.5 °C/2.0 °C warmer climate, Sci. Total Environ., 746, 140807, 10.1016/j.scitotenv.2020.140807
Li, 2018, Predictability of summer extreme precipitation days over eastern China, Clim. Dyn., 51, 4543, 10.1007/s00382-017-3848-x
Han, 2019, Seasonal prediction of midsummer extreme precipitation days over Northeast China, J. App. Meteorol. Climatol., 58, 2033, 10.1175/JAMC-D-18-0253.1
Wu, 2016, Monitoring urban expansion and its effects on land use and land cover changes in Guangzhou city, China, Environ. Monit. Assess., 188, 54, 10.1007/s10661-015-5069-2
Wang, 2020, Quantifying the response of potential flooding risk to urban growth in Beijing, Sci. Total Environ., 705, 135868, 10.1016/j.scitotenv.2019.135868
Wright, 2014, Critical examination of area reduction factors, J. Hydraul. Eng., 19, 769
Wright, D.B., Smith, J.A., Villarini, G., and Baeck, M. (2013). Applications of radar-based rainfall estimates to urban flood studies. J. Water Manag. Model., 21.
Chaubey, 1999, Uncertainty in the model parameters due to spatial variability of rainfall, J. Hydrol., 220, 48, 10.1016/S0022-1694(99)00063-3
Segond, 2007, The significance of spatial rainfall representation for flood runoff estimation: A numerical evaluation based on the lee catchment, UK, J. Hydrol., 347, 116, 10.1016/j.jhydrol.2007.09.040
Krvavica, N., and Rubinić, J. (2020). Evaluation of design storms and critical rainfall durations for flood prediction in partially urbanized catchments. Water, 12.
Yang, 2013, Urbanization and climate change: An examination of nonstationarities in urban flooding, J. Hydrometeorol., 14, 1791, 10.1175/JHM-D-12-095.1
Arnaud, 2002, Influence of rainfall spatial variability on flood prediction, J. Hydrol., 260, 216, 10.1016/S0022-1694(01)00611-4
Younger, 2009, Detecting the effects of spatial variability of rainfall on hydrological modelling within an uncertainty analysis framework, Hydrol. Process., 23, 1988, 10.1002/hyp.7341
Zoccatelli, 2010, Which rainfall spatial information for flash flood response modelling? A numerical investigation based on data from the Carpathian range, Romania, J. Hydrol., 394, 148, 10.1016/j.jhydrol.2010.07.019
Zhu, 2018, The impact of rainfall space-time structure in flood frequency analysis, Water Resour. Res., 54, 8983, 10.1029/2018WR023550
Bruni, 2015, On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution, Hydrol. Earth Syst. Sci., 19, 691, 10.5194/hess-19-691-2015
Hwang, 2020, Comparison of methods to estimate areal means of short duration rainfalls in small catchments, using rain gauge and radar data, J. Hydrol., 588, 125084, 10.1016/j.jhydrol.2020.125084
Chen, T., Ren, L., Yuan, F., Yang, X., Jiang, S., Tang, T., Liu, Y., Zhao, C., and Zhang, L. (2017). Comparison of spatial interpolation schemes for rainfall data and application in hydrological modeling. Water, 9.
Lin, 1988, Application of the step-duration orographic intensification factors method to estimation of PMP for mountainous regions, J. Hohai Univ., 3, 40
Zhou, 2020, The study of urban design storm based on stochastic storm transposition, Adv. Water Sci., 31, 583
Kong, 2016, Spatiotemporal patterns of global-continental-regional scale heavy rainfall, J. Beijing Norm. Univ., 2, 228
Cristiano, 2017, Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas - A review, Hydrol. Earth Sys. Sci., 21, 3859, 10.5194/hess-21-3859-2017
Donat, 2016, More extreme precipitation in the world’s dry and wet regions, Nat. Clim. Chang., 6, 508, 10.1038/nclimate2941
Patel, A., Goswami, A., Dharpure, J.K., and Thamban, M. (2020). Rainfall variability over the Indus, Ganga, and Brahmaputra river basins: A spatio-temporal characterisation. Quat. Int.
Smith, 2005, Extraordinary flood response of a small urban watershed to short duration convective rainfall, J. Hydrometeorol., 6, 599, 10.1175/JHM426.1
Guo, 2006, Mesoscale convective precipitation system modified by urbanization in Beijing City, Atmos. Res., 82, 112, 10.1016/j.atmosres.2005.12.007
Zhou, 2019, Storm catalog-based analysis of rainfall heterogeneity and frequency in a complex terrain, Water Resour. Res., 55, 1871, 10.1029/2018WR023567
Zhang, 2017, Assessment of rainfall spatial variability and its influence on runoff modelling: A case study in the Brue catchment, UK, Hydrol. Process., 31, 2972, 10.1002/hyp.11250
Liang, P., and Ding, Y. (2017). The long-term variation of extreme heavy precipitation and its link to urbanization effects in Shanghai during 1916–2014. Adv. Atmos. Sci., 321–334.
Mei, 2018, Multi-decadal spatial and temporal changes of extreme precipitation patterns in northern China (Jing-Jin-Ji district, 1960e2013), Quat. Int., 476, 1, 10.1016/j.quaint.2018.03.008
Zhang, 2014, Spatiotemporal variations of precipitation regimes across Yangtze River Basin, China, Theor. Appl. Climatol., 115, 703, 10.1007/s00704-013-0916-y
Bisht, 2017, Spatio-temporal trends of rainfall across Indian river basins, Theor. Appl. Climatol., 132, 419, 10.1007/s00704-017-2095-8
Thompson, 2018, Characterisation of heterogeneity and spatial autocorrelation in phase separating mixtures using Moran’s I, J. Colloid Interface Sci., 513, 180, 10.1016/j.jcis.2017.10.115
Kumari, 2019, Using Moran’s I and GIS to study the spatial pattern of land surface temperature in relation to land use/cover around a thermal power plant in Singrauli district, Madhya Pradesh, India, Remot. Sens. Appl. Soc. Environ., 15, 100239
Anselin, 1995, Local indicators of spatial association-LISA, Geogr. Anal., 27, 93, 10.1111/j.1538-4632.1995.tb00338.x
Song, W., Jia, H., Li, Z., and Tang, D. (2018). Using geographical semi-variogram method to quantify the difference between NO2 and PM2.5 spatial distribution characteristics in urban areas. Sci. Total Environ., 688–694.
Sun, 2012, Analysis and thinking on the extremes of the 21 July 2012 torrential rain in Beijing part II: Preliminary causation analysis and thinking, Meteorol. Mon., 10, 1267
Yin, 2016, Evaluating the impact and risk of pluvial flash flood on intra-urban road network: A case study in the city center of Shanghai, China, J. Hydrol., 537, 138, 10.1016/j.jhydrol.2016.03.037
Li, 2017, A statistical analysis of hourly heavy rainfall events over the Beijing metropolitan region during the warm seasons of 2007–2014, J. Climatol., 37, 4027, 10.1002/joc.4983
Liu, 2020, Dynamic spatial-temporal precipitation distribution models for short-duration rainstorms in Shenzhen, China based on machine learning, Atmos. Res., 237, 1, 10.1016/j.atmosres.2020.104861
Zhou, 2020, Comprehensive evaluation of latest GPM era IMERG and GSMaP precipitation products over mainland China, Atmos. Res., 246, 105132, 10.1016/j.atmosres.2020.105132
Li, 2021, Analysis of the spatial distribution of precipitation and topography with GPM data in the Tibetan Plateau, Atmos. Res., 247, 105259, 10.1016/j.atmosres.2020.105259
Hu, 2020, Global cropland intensification surpassed expansion between 2000 and 2010: A spatio-temporal analysis based on GlobeLand30, Sci. Total Environ., 746, 141035, 10.1016/j.scitotenv.2020.141035
Arsanjani, 2018, Characterizing, monitoring, and simulating land cover dynamics using GlobeLand30: A case study from 2000 to 2030, J. Environ. Manag., 214, 66, 10.1016/j.jenvman.2018.02.090
Douglas, 2000, Trends in floods and low flows in the United States: Impact of spatial correlation, J. Hydrol., 240, 90, 10.1016/S0022-1694(00)00336-X
Lettenmaier, 1994, Hydro-climatological trends in the continental United States, 1948–88, J. Clim., 7, 586, 10.1175/1520-0442(1994)007<0586:HCTITC>2.0.CO;2
Sen, 1968, Estimates of the regression coefficient based on kendall’s tau, J. Am. Stat. Assoc., 63, 1379, 10.1080/01621459.1968.10480934
Partal, 2006, Trend analysis in Turkish precipitation data, Hydrol. Process., 20, 2011, 10.1002/hyp.5993
Chen, L., Gao, Y., Zhu, D., Yuan, Y., and Liu, Y. (2019). Quantifying the scale effect in geospatial big data using semi-variograms. PLoS ONE, 14.
Ye, 2018, Spatial analysis of soil aggregate stability in a small catchment of the loess plateau, China: I. Spatial variability, Soil Tillage Res., 179, 71, 10.1016/j.still.2018.01.012
Ahmadi, 2007, Geostatistical analysis of spatial and temporal variations of groundwater level, Environ. Monit. Assess., 129, 277, 10.1007/s10661-006-9361-z
Garrigues, 2006, Quantifying spatial heterogeneity at the landscape scale using variogram models, Remot. Sens. Environ., 103, 81, 10.1016/j.rse.2006.03.013
Lee, 2015, Basin rotation method for analyzing the directional influence of moving storms on basin response, Stoch. Environ. Res. Risk Assess., 29, 251, 10.1007/s00477-014-0870-y
Foroud, 1984, Effects of a moving rainstorm on direct runoff properties, J. Am. Water Resour. Assoc., 20, 87, 10.1111/j.1752-1688.1984.tb04645.x