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A Regional Approach of Decadal Assessment of Extreme Precipitation Estimates: A Case Study in the Yangtze River Basin, China

  • Samiran DasEmail author
  • Dehua Zhu
  • Chi-Han Cheng
Article
  • 16 Downloads

Abstract

Changes in overall observed precipitation have been recognized in many parts of the world in recent decades, leading to the argument on climate change and its impact on extreme precipitation. However, the concept of natural variations and the complex physical mechanisms hidden in the observed data sets must also be taken into consideration. This study aims to examine the matter further with reference to inter-decadal variability in extreme precipitation quantiles appropriate for risk analysis. Temporal changes in extreme precipitation are assessed using a parametric approach incorporating a regional method in region-of-influence form. The index-flood method with the application of generalized extreme value distribution is used to estimate the decadal extreme precipitation. The study also performs a significance test to determine whether the decadal extremes are significant. A case study is performed on the Yangtze River Basin, where annual maximum 1-day precipitation data for 180 stations were analyzed over a 50-year period from 1961 to 2010. Extreme quantiles estimated from the 1990s data emerged as the significant values on several occasions. The immediate drop in the quantile values in the following decade, however, suggested that it is not practical to assign more weight to recent data for the quantile estimation process. The temporal patterns identified are in line with the previous studies conducted in the region and thus make it an alternative way to perform decadal analysis with an advantage that the scheme can be transferred to ungauged conditions.

Keywords

Regional frequency analysis extreme precipitation analysis decadal analysis hydrometeorology 

Notes

Acknowledgements

This study is supported by Nanjing University of Information Science and Technology in the form of a grant (grant no. 2243141501015) to the first author. Comments and suggestions from two anonymous reviewers are gratefully acknowledged.

References

  1. 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, 1–22.  https://doi.org/10.1029/2005JD006290.CrossRefGoogle Scholar
  2. Bengtsson, L., & Rana, A. (2014). Long-term change of daily and multi-daily precipitation in southern Sweden. Hydrological Processes,28, 2897–2911.  https://doi.org/10.1002/hyp.9774.CrossRefGoogle Scholar
  3. Bülow, I., Henrik, G., & Dan, M. (2015). Long term variations of extreme rainfall in Denmark and southern Sweden. Climate Dynamics.  https://doi.org/10.1007/s00382-014-2276-4.CrossRefGoogle Scholar
  4. Burn, D. H. (1990). Evaluation of regional flood frequency analysis with a region of influence approach. Water Resources Research,26, 2257–2265.CrossRefGoogle Scholar
  5. Chen, H., Sun, J., & Fan, K. (2012). Decadal features of heavy rainfall events in eastern China. Acta Meteorologica Sinica,26, 289–303.  https://doi.org/10.1007/s13351-012-0303-0.CrossRefGoogle Scholar
  6. Dalrymple, T. (1960). Flood frequency methods. U. S. Geological Survey,1543, 11–51.Google Scholar
  7. Das, S. (2017). Performance of region-of-influence approach of frequency analysis of extreme rainfall in monsoon climate conditions. International Journal of Climatology,37, 612–623.  https://doi.org/10.1002/joc.5025.CrossRefGoogle Scholar
  8. Das, S. (2018a). Goodness-of-fit tests for generalized normal distribution for use in hydrological frequency analysis. Pure and Applied Geophysics.  https://doi.org/10.1007/s00024-018-1877-y.CrossRefGoogle Scholar
  9. Das, S. (2018b). Extreme rainfall estimation at ungauged sites: Comparison between region-of-influence approach of regional analysis and spatial interpolation technique. International Journal of Climatology.  https://doi.org/10.1002/JOC.5819.CrossRefGoogle Scholar
  10. Das, S., & Cunnane, C. (2011). Examination of homogeneity of selected Irish pooling groups. Hydrology and Earth System Sciences,15, 819–830.  https://doi.org/10.5194/hess-15-819-2011.CrossRefGoogle Scholar
  11. Das, S., & Cunnane, C. (2012). Performance of flood frequency pooling analysis in a low CV context. Hydrological Sciences Journal,57, 433–444.  https://doi.org/10.1080/02626667.2012.666635.CrossRefGoogle Scholar
  12. Das, S., Millington, N., & Simonovic, S. P. (2013). Distribution choice for the assessment of design rainfall for the city of London (Ontario, Canada) under climate change. Canadian Journal of Civil Engineering,40, 121–129.  https://doi.org/10.1139/cjce-2011-0548.CrossRefGoogle Scholar
  13. Feng, S., Nadarajah, S., & Hu, Q. (2007). Modeling annual extreme precipitation in China using the generalized extreme value distribution. Journal of the Meteorological Society of Japan,85, 599–613.  https://doi.org/10.2151/jmsj.85.599.CrossRefGoogle Scholar
  14. Fu, G., Yu, J., Yu, X., et al. (2013). Temporal variation of extreme rainfall events in China, 1961–2009. Journal of Hydrology,487, 48–59.  https://doi.org/10.1016/j.jhydrol.2013.02.021.CrossRefGoogle Scholar
  15. Gaál, L., & Kyselý, J. (2009). Comparison of region-of-influence methods for estimating high quantiles of precipitation in a dense dataset in the Czech Republic. Hydrology and Earth System Sciences,13, 2203–2219.  https://doi.org/10.5194/hess-13-2203-2009.CrossRefGoogle Scholar
  16. Gocic, M., & Trajkovic, S. (2013). Analysis of changes in meteorological variables using Mann–Kendall and Sen’s slope estimator statistical tests in Serbia. Global and Planetary Change,100, 172–182.  https://doi.org/10.1016/j.gloplacha.2012.10.014.CrossRefGoogle Scholar
  17. Gong, D.-Y., & Ho, C.-H. (2002). Shift in the summer rainfall over the Yangtze River valley in the late 1970s. Geophysical Research Letters,29, 78-1–78-4.  https://doi.org/10.1029/2001gl014523.CrossRefGoogle Scholar
  18. Gong, D. Y., & Wang, S. W. (2000). Severe summer rainfall in China associated with the enhanced global warming. Climate Research,16, 51–59.  https://doi.org/10.3354/cr016051.CrossRefGoogle Scholar
  19. Gultepe, I., Heymsfield, A. J., Gallagher, M., et al. (2017). Ice fog: The current state of knowledge and future challenges. Meteorological Monographs,58, 41–424.  https://doi.org/10.1175/amsmonographs-d-17-0002.1.CrossRefGoogle Scholar
  20. Gultepe, I., Isaac, G. A., Joe, P., et al. (2014). Roundhouse (RND) mountain top research site: Measurements and uncertainties for winter alpine weather conditions. Pure and Applied Geophysics,171, 59–85.  https://doi.org/10.1007/s00024-012-0582-5.CrossRefGoogle Scholar
  21. Guo, J., Chen, H., Xu, C. Y., et al. (2012). Prediction of variability of precipitation in the Yangtze River Basin under the climate change conditions based on automated statistical downscaling. Stochastic Environmental Research and Risk Assessment,26, 157–176.  https://doi.org/10.1007/s00477-011-0464-x.CrossRefGoogle Scholar
  22. Guo, J., Guo, S., Li, Y., et al. (2013). Spatial and temporal variation of extreme precipitation indices in the Yangtze River basin, China. Stochastic Environmental Research and Risk Assessment,27, 459–475.  https://doi.org/10.1007/s00477-012-0643-4.CrossRefGoogle Scholar
  23. Guo, P., Zhang, X., Zhang, S., et al. (2014). Decadal variability of extreme precipitation days over northwest China from 1963 to 2012. Journal of Meteorological Research,28, 1099–1113.  https://doi.org/10.1007/s13351-014-4022-6.1.CrossRefGoogle Scholar
  24. Hosking, J. R. M., & Wallis, J. R. (1993). Some statistics useful in regional frequency analysis. Water Resources Research,29, 271–281.CrossRefGoogle Scholar
  25. Hosking, J. R. M., & Wallis, J. R. (1997). Regional frequency analysis: An approach based on L-moments. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  26. Huang, H., Winter, J. M., Osterberg, E. C., et al. (2017). Total and extreme precipitation changes over the Northeastern United States. Journal of Hydrometeorology,18, 1783–1798.  https://doi.org/10.1175/JHM-D-16-0195.1.CrossRefGoogle Scholar
  27. Institute of Hydrology. (1999). Flood Estimation Handbook (Vol. 1-5). Wallingford: Institute of Hydrology.Google Scholar
  28. IPCC. (2001). Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press.Google Scholar
  29. Jiang, T., Su, B., & Hartmann, H. (2007). Temporal and spatial trends of precipitation and river flow in the Yangtze River Basin, 1961–2000. Geomorphology,85, 143–154.  https://doi.org/10.1016/j.geomorph.2006.03.015.CrossRefGoogle Scholar
  30. Ju, Q., Yu, Z., Hao, Z., et al. (2014). Response of hydrologic processes to future climate changes in the Yangtze River Basin. Journal of Hydrologic Engineering.  https://doi.org/10.1061/(asce)he.1943-5584.0000770.CrossRefGoogle Scholar
  31. Jung, Y., Shin, J. Y., Ahn, H., & Heo, J. H. (2017). The spatial and temporal structure of extreme rainfall trends in South Korea. Water (Switzerland).  https://doi.org/10.3390/w9100809.CrossRefGoogle Scholar
  32. Kay, A. L., Jones, D. A., Crooks, S. M., et al. (2007). An investigation of site-similarity approaches to generalisation of a rainfall–runoff model. Hydrology and Earth System Sciences,11, 500–515.  https://doi.org/10.5194/hess-11-500-2007.CrossRefGoogle Scholar
  33. Kyselý, J., Gaál, L., & Picek, J. (2011). Comparison of regional and at-site approaches to modelling probabilities of heavy precipitation. International Journal of Climatology,31, 1457–1472.  https://doi.org/10.1002/joc.2182.CrossRefGoogle Scholar
  34. Liu, Y., Huang, G., & Huang, R. (2011). Inter-decadal variability of summer rainfall in Eastern China detected by the Lepage test. Hydrology and Earth System Sciences.  https://doi.org/10.1007/s00704-011-0442-8.CrossRefGoogle Scholar
  35. Nichols, M. H., Renard, K. G., & Osborn, H. B. (2002). Precipitation changes from 1956 to 1996 on the Walnut Gulch Experimental Watershed. Journal of the American Water Resources Association,38, 161–172.CrossRefGoogle Scholar
  36. Ntegeka, V., & Willems, P. (2008). Trends and multidecadal oscillations in rainfall extremes, based on a more than 100-year time series of 10 min rainfall intensities at Uccle, Belgium. Water Resources Research.  https://doi.org/10.1029/2007wr006471.CrossRefGoogle Scholar
  37. Pedron, I. T., Silva Dias, M. A. F., de Paula, Dias S., et al. (2017). Trends and variability in extremes of precipitation in Curitiba—Southern Brazil. International Journal of Climatology,37, 1250–1264.  https://doi.org/10.1002/joc.4773.CrossRefGoogle Scholar
  38. Qian, W., Fu, J., & Yan, Z. (2007). Decrease of light rain events in summer associated with a warming environment in China during 1961–2005. Geophysical Research Letters,34, 1–5.  https://doi.org/10.1029/2007GL029631.CrossRefGoogle Scholar
  39. Rasmussen, R., Baker, B., Kochendorfer, J., et al. (2012). How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed. Bulletin of the American Meteorological Society,93, 811–829.  https://doi.org/10.1175/BAMS-D-11-00052.1.CrossRefGoogle Scholar
  40. Reed, D. W., Faulkner, D. S., & Stewart, E. J. (1999). The FORGEX method of rainfall growth estimation II: Description. Hydrology and Earth System Sciences,3, 197–203.  https://doi.org/10.5194/hess-3-205-1999.CrossRefGoogle Scholar
  41. Scherrer, S. C., Fischer, E. M., Posselt, R., Liniger, M. A., Croci-Maspoli, M., & Knutti, R. (2016). Emerging trends in heavy precipitation and hot temperature extremes in Switzerland. Journal of Geophysical Research: Atmospheres, 121(6), 2626–2637.  https://doi.org/10.1002/2015JD024634.CrossRefGoogle Scholar
  42. Serinaldi, F., & Kilsby, C. G. (2016). The importance of prewhitening in change point analysis under persistence. Stochastic Environmental Research and Risk Assessment,30, 763–777.  https://doi.org/10.1007/s00477-015-1041-5.CrossRefGoogle Scholar
  43. Soltani, M., Laux, P., Kunstmann, H., et al. (2016). Assessment of climate variations in temperature and precipitation extreme events over Iran. Theoretical and Applied Climatology,126, 775–795.  https://doi.org/10.1007/s00704-015-1609-5.CrossRefGoogle Scholar
  44. Su, B., Gemmer, M., & Jiang, T. (2008). Spatial and temporal variation of extreme precipitation over the Yangtze River Basin. Quaternary International,186, 22–31.  https://doi.org/10.1016/j.quaint.2007.09.001.CrossRefGoogle Scholar
  45. Su, B. D., Jiang, T., & Jin, W. B. (2006). Recent trends in observed temperature and precipitation extremes in the Yangtze River basin, China. Theoretical and Applied Climatology,83, 139–151.  https://doi.org/10.1007/s00704-005-0139-y.CrossRefGoogle Scholar
  46. Svensson, C., & Jones, D. A. (2010). Review of rainfall frequency estimation methods. Journal of Flood Risk Management.  https://doi.org/10.1111/j.1753-318x.2010.01079.x/abstract.CrossRefGoogle Scholar
  47. Tabari, H., AghaKouchak, A., & Willems, P. (2014). A perturbation approach for assessing trends in precipitation extremes across Iran. Journal of Hydrology,519, 1420–1427.  https://doi.org/10.1016/j.jhydrol.2014.09.019.CrossRefGoogle Scholar
  48. Tabari, H., & Willems, P. (2016). Daily precipitation extremes in Iran: Decadal anomalies. Journal of the American Water Resources Association.  https://doi.org/10.1111/1752-1688.12403.CrossRefGoogle Scholar
  49. Tsonis, A. A. (1996). Widespread increases in low-frequency variability of precipitation over the past century. Nature,382, 700.CrossRefGoogle Scholar
  50. Turkes, M. (1996). Spatial and temporal analysis of annual rainfall variations I. Journal of Climatology,1076, 1057–1076.CrossRefGoogle Scholar
  51. Viglione, A., Laio, F., & Claps, P. (2007). A comparison of homogeneity tests for regional frequency analysis. Water Resources Research.  https://doi.org/10.1029/2006WR005095.CrossRefGoogle Scholar
  52. Wallis, J. R., Schaefer, M. G., Barker, B. L., & Taylor, G. H. (2007). Regional precipitation-frequency analysis and spatial mapping for 24-hour and 2-hour durations for Washington State. Hydrology and Earth System Sciences,11, 415–442.  https://doi.org/10.5194/hess-11-415-2007.CrossRefGoogle Scholar
  53. Wang, Y., & Zhou, L. (2005). Observed trends in extreme precipitation events in China during 1961–2001 and the associated changes in large-scale circulation. Geophysical Research Letters,32, 1–4.  https://doi.org/10.1029/2005GL022574.CrossRefGoogle Scholar
  54. Willems, P. (2013). Adjustment of extreme rainfall statistics accounting for multidecadal climate oscillations. Journal of Hydrology,490, 126–133.  https://doi.org/10.1016/j.jhydrol.2013.03.034.CrossRefGoogle Scholar
  55. Yilmaz, A. G., Hossain, I., & Perera, B. J. C. (2014). Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: A case study of Melbourne. Hydrology and Earth System Sciences,1, 1.  https://doi.org/10.5194/hess-18-4065-2014.CrossRefGoogle Scholar
  56. Zhai, P., Zhang, X., Wan, H., & Pan, X. (2005). Trends in total precipitation and frequency of daily precipitation extremes over China. Journal of Climate,18, 1096–1108.  https://doi.org/10.1175/JCLI-3318.1.CrossRefGoogle Scholar
  57. Zhang, Q., Xu, C. Y., Zhang, Z., et al. (2008). Spatial and temporal variability of precipitation maxima during 1960–2005 in the Yangtze River basin and possible association with large-scale circulation. Journal of Hydrology,353, 215–227.  https://doi.org/10.1016/j.jhydrol.2007.11.023.CrossRefGoogle Scholar
  58. Zhang, X., Zwiers, F. W., Hegerl, G. C., et al. (2007). Detection of human influence on twentieth-century precipitation trends. Nature,448, 461–465.  https://doi.org/10.1038/nature06025.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Hydrology and Water ResourcesNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Taiwan Research Institute on Water Resources and AgricultureNew TaipeiTaiwan, R.O.C.

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