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Wavelet-Based Detection of Time-Frequency Changes for Monthly Rainfall and SPI Series in Taiwan

  • Jenq-Tzong ShiauEmail author
  • Yun-Feng Chiu
Original Article
  • 3 Downloads

Abstract

This study aims to assess time-frequency changes and trends of both monthly rainfall and SPI (standardized precipitation index) series in Taiwan using wavelet transform and Mann-Kendall test. The monthly data at Taipei, Sunmoon Lake, Kaohsiung, and Dawu stations with recorded length of 118, 73, 83, and 75 years, respectively, are used. The results of MK test reveal that insignificant positive monotonic trends for both rainfall and SPI series at west-side Taiwan (Taipei, Sunmoon Lake, and Kaohsiung stations) are observed, while both series have significant negative trends at Dawu station located at southeastern Taiwan. Wavelet analyses on the rainfall and SPI series indicate similar variation of wavelet power spectrum over time except the noticeable one-year periodicity in rainfall series and less power spectrum in SPI series. It is worth to note that the sub-decadal 36- and 96-month periodicities are common, but insignificant, at four stations for both series, although different data lengths are used. Combined with MK-test and wavelet-analysis results reveal that slightly less severe and less frequent droughts occur at Taipei and Sunmoon Lake stations, while drought frequencies probably remain unchanged at Kaohsiung and Dawu stations with slightly less severe and more severe droughts respectively occur at these two stations.

Keywords

Time-frequency change Wavelet transform Mann-Kendall test Standardized precipitation index 

Notes

Acknowledgments

This research was partly supported by the Ministry of Science and Technology, Taiwan, ROC (MOST 104-2221-E-006-174).

References

  1. Adamowski, K., Prokoph, A., Adamowski, J.: Development of a new method of wavelet aided trend detection and estimation. Hydrol. Process. 23(18), 2686–2696 (2009)Google Scholar
  2. Amiri, E.: Forecasting daily river flows using nonlinear time series models. J. Hydrol. 527, 1054–1072 (2015)Google Scholar
  3. Bayazit, M., Önöz, G., Aksoy, H.: Nonparametric streamflow simulation by wavelet or Fourier analysis. Hydrol. Sci. J. 46(4), 623–634 (2001)Google Scholar
  4. Beecham, S., Chowdhury, R.K.: Temporal characteristics and variability of point rainfall: a statistical and wavelet analysis. Int. J. Climatol. 30(3), 458–473 (2010)Google Scholar
  5. Farge, M.: Wavelet transforms and their applications to turbulence. Annu. Rev. Fluid Mech. 24, 395–457 (1992)Google Scholar
  6. Fischer, T., Gemmer, M., Su, B., Scholten, T.: Hydrological long-term dry and wet periods in the Xijiang River basin, South China. Hydrol. Earth Syst. Sci. 17(1), 135–148 (2013)Google Scholar
  7. Golian, S., Mazdiyasni, A., AghaKouchak, A.: Trend in meteorological and agricultural droughts in Iran. Theor. Appl. Climatol. 119(3–4), 679–688 (2015)Google Scholar
  8. Hamed, K.H.: Trend detection in hydrologic data: the Mann-Kendall trend test under scaling hypothesis. J. Hydrol. 349(3–4), 350–363 (2008)Google Scholar
  9. Hirsch, R.M., Slack, J.R., Smith, R.A.: Techniques of trend analysis for monthly water quality data. Water Resour. Res. 18(1), 107–121 (1982)Google Scholar
  10. Jiang, P., Yu, Z., Gautam, M.R., Acharya, K.: The spatiotemporal characteristics of extreme precipitation events in the Western United States. Water Resour. Manag. 30(13), 4807–4821 (2016)Google Scholar
  11. Jukić, D., Denić-Jukić, V.: Partial spectral analysis of hydrologic time series. J. Hydrol. 400, 223–233 (2011)Google Scholar
  12. Kalteh, A.M.: Wavelet genetic algorithm-support vector regression (wavelet GA-SVR) for monthly flow forecasting. Water Resour. Manag. 29(4), 1283–1293 (2015)Google Scholar
  13. Kalteh, A.M., Hjorth, P.: Imputation of missing values in a precipitation-runoff process database. Hydrol. Res. 40(4), 420–432 (2009)Google Scholar
  14. Kendall, M.G.: Rank Correlation Methods. Griffin, London (1975)Google Scholar
  15. Kişi, Ö., Partal, T.: Wavelet and neuro-fuzzy conjunction model for streamflow forecasting. Hydrol. Res. 42(6), 447–456 (2011)Google Scholar
  16. Kousari, M.R., Dastorani, M.T., Niazi, Y., Soheili, E., Hayatzadeh, M., Chezgi, J.: Trend detection of drought in arid and semi-arid regions of Iran based on implementation of reconnaissance drought index (RDI) and application of non-parametrical statistical method. Water Resour. Manag. 28(7), 1857–1872 (2014)Google Scholar
  17. Labat, D.: Recent advances in wavelet analyses: part 1: a review of concepts. J. Hydrol. 314, 275–288 (2005)Google Scholar
  18. Lohani, A.K., Kumar, R., Singh, R.D.: Hydrological time series modelling: a comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J. Hydrol. 442, 23–35 (2012)Google Scholar
  19. Mann, H.B.: Nonparametric tests against trend. Econometrica. 13, 245–259 (1945)Google Scholar
  20. McKee, T.B., Doesken, N.J., Kleist, J.: The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology, pp. 179–184 (1993)Google Scholar
  21. Mishra, A.K., Singh, V.P.: A review of drought concepts. J. Hydrol. 391(1–2), 202–216 (2010)Google Scholar
  22. Modarres, R., Ouarda, T.B.M.J.: Generalized autoregressive conditional heteroscedasticity modelling of hydrologic time series. Hydrol. Process. 27(22), 3174–3191 (2013)Google Scholar
  23. Mondal, M.S., Chowdhury, J.U.: Generation of 10-day flow of the Brahmaputra River using a time series model. Hydrol. Res. 44(6), 1071–1083 (2013)Google Scholar
  24. Moreira, E.E., Martins, D.S., Pereira, L.S.: Assessing drought cycles in SPI time series using Fourier analysis. Nat. Hazards Earth Syst. Sci. 15(3), 571–585 (2015)Google Scholar
  25. Nakken, M.: Wavelet analysis of rainfall-runoff variability isolating climatic from anthropogenic pattern. Environ. Model. Softw. 14(4), 283–295 (1999)Google Scholar
  26. Nerini, D., Besic, N., Sideris, I., Germann, U., Foresti, L.: A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform. Hydrol. Earth Syst. Sci. 21(6), 2777–2797 (2017)Google Scholar
  27. Özger, M., Mishra, A.K., Singh, V.P.: Low frequency drought variability associated with climate indices. J. Hydrol. 364, 152–162 (2009)Google Scholar
  28. Prabhakar, A.K., Singh, K.K., Lohani, A.K.: Regional level long-term rainfall variability assessment using Mann-Kendall test over the Odisha state of India. J Agrometeorology. 20(2), 164–165 (2018)Google Scholar
  29. Rashid, M.M., Beecham, S., Chowdhury, R.K.: Assessment of trends in point rainfall using continuous wavelet transform. Adv. Water Resour. 82, 1–15 (2015)Google Scholar
  30. Salas, J. D.: Analysis and modeling of hydrologic time series. in Handbook of Hydrology, edited by D. R. Maidment, McGraw- Hill Inc. (1993)Google Scholar
  31. Sang, Y.F.: A review on the applications of wavelet transform in hydrology time series analysis. Atmos. Res. 122, 8–15 (2013)Google Scholar
  32. Shadmani, M., Marofi, S., Roknian, M.: Trend analysis in reference evapotranspiration using Mann-Kendall and Spearman’s rho tests in arid regions of Iran. Water Resour. Manag. 26(1), 211–224 (2012)Google Scholar
  33. Shiau, J.T., Huang, C.Y.: Detecting multi-purpose reservoir operation induced time-frequency alteration using wavelet transform. Water Resour. Manag. 28(11), 3577–3590 (2014)Google Scholar
  34. Shiau, J.T., Lin, J.W.: Clustering quantile regression-based drought trends in Taiwan. Water Resour. Manag. 30(3), 1053–1069 (2016)Google Scholar
  35. Telesca, L., Vicente-Serrano, S.M., López-Moreno, J.I.: Power spectral characteristics of drought in the Ebro river basin at different temporal scales. Stoch. Env. Res. Risk A. 27(5), 1155–1170 (2013)Google Scholar
  36. Todd, B., Macdonald, N., Chiverrell, R.C., Caminade, C., Hooke, J.M.: Severity, duration and frequency of drought in SE England from 1697 to 2011. Clim. Chang. 121(4), 673–687 (2013)Google Scholar
  37. Torrence, C., Compo, G.P.: A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79(1), 61–78 (1998)Google Scholar
  38. Wu, F.C., Chang, C.F., Shiau, J.T.: Assessment of flow regime alterations over a spectrum of temporal scales using wavelet-based approaches. Water Resour. Res. 51(5), 3317–3338 (2015)Google Scholar
  39. Zolezzi, G., Bellin, A., Bruno, M.C., Maiolini, B., Siviglia, A.: Assessing hydrologic alterations at multiple temporal scales: Adige River, Italy. Water Resour. Res. 45, W12421 (2009).  https://doi.org/10.1029/2008WR007226 Google Scholar

Copyright information

© Korean Meteorological Society and Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Hydraulic and Ocean EngineeringNational Cheng Kung UniversityTainanTaiwan, Republic of China

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